{ "schema_version": "1.1", "id": "atlas-agent-reliability", "slug": "agent-reliability", "title": "Agent Reliability", "url": "https://feed7.dev/atlas/agent-reliability", "current_answer": null, "implementation_consequence": null, "agent_context": null, "confidence": "auto_collected", "last_verified": null, "last_updated": null, "evidence": [ { "schema_version": "1.1", "id": "p14", "slug": "linkedin-eval-rollout", "url": "https://feed7.dev/p/linkedin-eval-rollout", "title": "Rolling out agents behind evals — an operator’s playbook", "why_included": "Concrete staged-rollout playbook with numbers — but the claimed win rates are not yet source-linked.", "summary": "Operator describes gating an internal agent behind a 40-case eval, canarying to 10% of tasks, then expanding. Claims 30% fewer escalations.", "practical_implication": "The staging pattern is reusable today; treat the win-rate numbers as unverified until the promised write-up lands.", "agent_context": "Staged agent rollout: gate behind eval set, canary 10% of tasks, expand on pass. Pattern is sound; the 30% improvement claim is unverified.", "source": { "name": "LinkedIn", "url": "https://www.linkedin.com/posts/operator-evals-rollout", "published_at": "2026-07-01T00:00:00.000Z" }, "source_class": "social_media", "content_type": "Social Thread", "layer": "benchmark", "domains": [ "coding" ], "topics": [ "agent-evals", "agent-reliability" ], "verification": { "status": "needs_review", "label": "Needs Review", "method": "unverified", "verified_at": null }, "uncertainty": [ "Win-rate numbers not source-linked; write-up promised but not published." ], "connected_context": null, "lifecycle": "New", "published_at": "2026-07-01T00:00:00.000Z", "modified_at": "2026-07-01T00:00:00.000Z", "supersedes": [], "expires_at": null, "formats": { "html": "https://feed7.dev/p/linkedin-eval-rollout", "json": "https://feed7.dev/p/linkedin-eval-rollout.json", "markdown": "https://feed7.dev/p/linkedin-eval-rollout.md" } }, { "schema_version": "1.1", "id": "auto-3518047f01", "slug": "how-evals-and-prompts-shape-agent-behavior-preetika-bhat-3518047f01", "url": "https://feed7.dev/p/how-evals-and-prompts-shape-agent-behavior-preetika-bhat-3518047f01", "title": "How Evals and Prompts Shape Agent Behavior — Preetika Bhateja & Daniel Bump, YouTube Ads", "why_included": "Start with a few core tasks, inspect traces, change one component at a time, and protect a separate test set.", "summary": "Production agent behavior should be improved through a loop of tool design, small eval sets, trace review, and calibrated judges—not prompt tweaks based on one bad run.", "practical_implication": "Build evals early, inspect traces, and change one prompt, tool, rubric, or model component at a time. Compare human and LLM ratings, maintain a high-agreement golden set, and use ablations to establish whether a change actually improves the agent.", "agent_context": "The YouTube Ads team recommends optimizing a focused set of agent tools before larger evals, then starting with **a few core tasks** and testing both desired behavior and prohibited actions. Explanations help diagnose multi-dimensional outcomes such as accuracy and brand safety.\n\nBuild evals early, inspect traces, and change one prompt, tool, rubric, or model component at a time. Compare human and LLM ratings, maintain a high-agreement golden set, and use ablations to establish whether a change actually improves the agent.\n\nDo not tune against isolated nondeterministic failures. Keep a sparingly used **test set**, refresh it with production data, monitor judge disagreement, and define launch gates before iteration makes the rubric a moving target.", "source": { "name": "AI Engineer", "url": "https://www.youtube.com/watch?v=xyL2Ltkh-SA", "published_at": "2026-07-24T00:00:00.000Z" }, "source_class": "video", "content_type": "Video", "layer": "benchmark", "domains": [ "coding" ], "topics": [ "agent-evals", "agent-reliability", "prompting" ], "verification": { "status": "source_linked", "label": "Source Linked", "method": "source_feed", "verified_at": null }, "uncertainty": [], "connected_context": null, "lifecycle": "New", "published_at": "2026-07-24T00:00:00.000Z", "modified_at": "2026-07-24T00:00:00.000Z", "supersedes": [], "expires_at": null, "formats": { "html": "https://feed7.dev/p/how-evals-and-prompts-shape-agent-behavior-preetika-bhat-3518047f01", "json": "https://feed7.dev/p/how-evals-and-prompts-shape-agent-behavior-preetika-bhat-3518047f01.json", "markdown": "https://feed7.dev/p/how-evals-and-prompts-shape-agent-behavior-preetika-bhat-3518047f01.md" } }, { "schema_version": "1.1", "id": "auto-db6b4984d3", "slug": "harness-engineering-is-not-enough-why-software-factories-db6b4984d3", "url": "https://feed7.dev/p/harness-engineering-is-not-enough-why-software-factories-db6b4984d3", "title": "Harness Engineering is not Enough: Why Software Factories Fail — Dex Horthy, HumanLayer", "why_included": "Agree on design before implementation and build vertical slices so generated changes remain readable and testable.", "summary": "Coding-agent loops can raise throughput without preserving maintainability. Keep human ownership of code, and use upfront alignment to make review affordable instead of trying to automate it away.", "practical_implication": "Keep humans responsible for the resulting code. Use model-assisted planning, agree on design before implementation, and build in vertical slices so every generated change remains practical to read and test.", "agent_context": "The talk argues that coding models are rewarded mainly when **code runs and tests pass**, not when architecture remains easy to change. Review agents and extra loops can raise the floor, but cannot supply a missing maintainability signal.\n\nKeep humans responsible for the resulting code. Use **model-assisted planning**, agree on design before implementation, and build in vertical slices so every generated change remains practical to read and test.\n\nThere is no established benchmark here that proves how well current models preserve codebase quality. Longer-task evaluations such as **SWE Marathon**, DeepSuite, and FrontierCode may help, but model-based quality judges have their own ceiling.", "source": { "name": "AI Engineer", "url": "https://www.youtube.com/watch?v=Ib5GBkD555M", "published_at": "2026-07-23T00:00:00.000Z" }, "source_class": "video", "content_type": "Video", "layer": "agent", "domains": [ "coding" ], "topics": [ "harness-engineering", "agent-reliability", "agent-evals" ], "verification": { "status": "source_linked", "label": "Source Linked", "method": "source_feed", "verified_at": null }, "uncertainty": [], "connected_context": null, "lifecycle": "New", "published_at": "2026-07-23T00:00:00.000Z", "modified_at": "2026-07-23T00:00:00.000Z", "supersedes": [], "expires_at": null, "formats": { "html": "https://feed7.dev/p/harness-engineering-is-not-enough-why-software-factories-db6b4984d3", "json": "https://feed7.dev/p/harness-engineering-is-not-enough-why-software-factories-db6b4984d3.json", "markdown": "https://feed7.dev/p/harness-engineering-is-not-enough-why-software-factories-db6b4984d3.md" } }, { "schema_version": "1.1", "id": "auto-179f4fe13f", "slug": "ai-gateway-logs-now-have-a-dedicated-page-179f4fe13f", "url": "https://feed7.dev/p/ai-gateway-logs-now-have-a-dedicated-page-179f4fe13f", "title": "AI Gateway logs now have a dedicated page", "why_included": "Trace cost, tokens, latency, routing, cache use, and provider fallbacks by request ID, then export filtered runs for analysis.", "summary": "AI Gateway’s dedicated logs expose per-request cost, tokens, latency, routing, and provider fallbacks, making agent failures and spend anomalies easier to trace.", "practical_implication": "Use filters and request-ID search to debug agent failures or compare routing behavior. Filtered views are shareable by URL and exportable as CSV or JSON, which makes targeted cost and reliability analysis easier.", "agent_context": "The new **Logs page** lists gateway requests with cost, token counts, duration, model, provider, and region. Request details include time to first token, cache and reasoning tokens, policy flags, and the ordered fallback path.\n\nUse filters and request-ID search to debug agent failures or compare routing behavior. Filtered views are shareable by URL and exportable as **CSV or JSON**, which makes targeted cost and reliability analysis easier.\n\nThe page records what happened but does not define alerts, retention, or automated remediation. Diagnosis still requires interpreting provider failures, routing-budget exhaustion, and latency in the context of the agent run.", "source": { "name": "Vercel", "url": "https://vercel.com/changelog/ai-gateway-logs", "published_at": "2026-07-31T00:00:00.000Z" }, "source_class": "blog_post", "content_type": "Engineering Post", "layer": "infra", "domains": [ "coding", "data" ], "topics": [ "observability", "gateways", "agent-reliability" ], "verification": { "status": "official_source", "label": "Official Source", "method": "source_feed", "verified_at": null }, "uncertainty": [], "connected_context": null, "lifecycle": "New", "published_at": "2026-07-31T00:00:00.000Z", "modified_at": "2026-07-31T00:00:00.000Z", "supersedes": [], "expires_at": null, "formats": { "html": "https://feed7.dev/p/ai-gateway-logs-now-have-a-dedicated-page-179f4fe13f", "json": "https://feed7.dev/p/ai-gateway-logs-now-have-a-dedicated-page-179f4fe13f.json", "markdown": "https://feed7.dev/p/ai-gateway-logs-now-have-a-dedicated-page-179f4fe13f.md" } }, { "schema_version": "1.1", "id": "auto-fb423aadb9", "slug": "run-multiple-isolated-agents-in-a-single-sandbox-fb423aadb9", "url": "https://feed7.dev/p/run-multiple-isolated-agents-in-a-single-sandbox-fb423aadb9", "title": "Run multiple isolated agents in a single Sandbox", "why_included": "Give each agent a private Linux user and expose only selected collaboration files through a shared group directory.", "summary": "Vercel Sandbox can now isolate agents as Linux users while exposing selected files through a shared group directory. This gives multi-agent harnesses a simpler permission boundary.", "practical_implication": "Create one user per agent and expose only the workspace they need through a group. This makes coder-reviewer or other multi-agent roles easier to separate without provisioning a sandbox for each role.", "agent_context": "The **@vercel/sandbox SDK** now supports multiple Linux users and groups. Each agent gets a **private home directory**; users cannot read, write, or list one another’s files, while a **shared group directory** supports collaboration.\n\nCreate one user per agent and expose only the workspace they need through a group. This makes coder-reviewer or other multi-agent roles easier to separate without provisioning a sandbox for each role.\n\nThe boundary is user and group permissions inside one Sandbox. The material does not claim separate kernels, resource quotas, or protection from vulnerabilities that cross operating-system user boundaries.", "source": { "name": "Vercel", "url": "https://vercel.com/changelog/run-multiple-isolated-agents-in-a-single-sandbox", "published_at": "2026-07-30T00:00:00.000Z" }, "source_class": "blog_post", "content_type": "Engineering Post", "layer": "infra", "domains": [ "coding", "security" ], "topics": [ "sandboxing", "multi-agent", "agent-reliability" ], "verification": { "status": "official_source", "label": "Official Source", "method": "source_feed", "verified_at": null }, "uncertainty": [], "connected_context": null, "lifecycle": "New", "published_at": "2026-07-30T00:00:00.000Z", "modified_at": "2026-07-30T00:00:00.000Z", "supersedes": [], "expires_at": null, "formats": { "html": "https://feed7.dev/p/run-multiple-isolated-agents-in-a-single-sandbox-fb423aadb9", "json": "https://feed7.dev/p/run-multiple-isolated-agents-in-a-single-sandbox-fb423aadb9.json", "markdown": "https://feed7.dev/p/run-multiple-isolated-agents-in-a-single-sandbox-fb423aadb9.md" } }, { "schema_version": "1.1", "id": "auto-9e82a687ae", "slug": "benchmarking-coding-agents-on-new-vs-legacy-codebases-de-9e82a687ae", "url": "https://feed7.dev/p/benchmarking-coding-agents-on-new-vs-legacy-codebases-de-9e82a687ae", "title": "Benchmarking Coding Agents on New vs Legacy Codebases — Denys Linkov, Wisedocs", "why_included": "Judge coding agents with explicit requirements and end-to-end tests, since fast output may be incomplete scaffolding.", "summary": "A production refactor shows why coding-agent evaluations need acceptance criteria and end-to-end verification: fast output can still be incomplete scaffolding.", "practical_implication": "Benchmark agents against explicit requirements, runnable end-to-end tests, deployment constraints, and hidden assumptions—not elapsed time or lines changed. Wisedocs also found a monorepo simpler for verification and sandbox setup across its former 10+ repositories.", "agent_context": "An early O3-assisted task took **3 hours** and produced 10 major mistakes; newer Sonnet 4.6 solved it after one extra iteration and Opus 4.8 nearly one-shot it. A broader GPT-5.5 attempt finished in **10m 22s** but mostly wrote scaffolding.\n\nBenchmark agents against explicit requirements, runnable end-to-end tests, deployment constraints, and hidden assumptions—not elapsed time or lines changed. Wisedocs also found a monorepo simpler for verification and sandbox setup across its former **10+ repositories**.\n\nThese are task-specific observations from one refactor, not controlled cross-model results. Human review remained part of the project, and only **15 of 17 requirements** were met during the migration.", "source": { "name": "AI Engineer", "url": "https://www.youtube.com/watch?v=7vn4WpqNpck", "published_at": "2026-08-08T00:00:00.000Z" }, "source_class": "video", "content_type": "Video", "layer": "benchmark", "domains": [ "coding" ], "topics": [ "agent-evals", "agent-reliability", "coding-agents" ], "verification": { "status": "source_linked", "label": "Source Linked", "method": "source_feed", "verified_at": null }, "uncertainty": [], "connected_context": null, "lifecycle": "New", "published_at": "2026-08-08T00:00:00.000Z", "modified_at": "2026-08-08T00:00:00.000Z", "supersedes": [], "expires_at": null, "formats": { "html": "https://feed7.dev/p/benchmarking-coding-agents-on-new-vs-legacy-codebases-de-9e82a687ae", "json": "https://feed7.dev/p/benchmarking-coding-agents-on-new-vs-legacy-codebases-de-9e82a687ae.json", "markdown": "https://feed7.dev/p/benchmarking-coding-agents-on-new-vs-legacy-codebases-de-9e82a687ae.md" } }, { "schema_version": "1.1", "id": "auto-91ef3aae9d", "slug": "learning-when-to-trust-via-selective-context-preference--91ef3aae9d", "url": "https://feed7.dev/p/learning-when-to-trust-via-selective-context-preference--91ef3aae9d", "title": "Learning When to Trust via Selective Context Preference Optimization", "why_included": "Test whether agents use good context while resisting misleading context, rather than appearing robust by ignoring evidence.", "summary": "MIST tests whether models use good context while resisting bad context, exposing agents that appear robust only because they ignore external evidence altogether.", "practical_implication": "Evaluate retrieval-augmented agents for selective trust, not only prompt-injection resistance. The proposed SCOPE method trains on matched preference pairs balanced across all four conditions so resistance does not come from ignoring useful context.", "agent_context": "MIST renders each reasoning item under **four matched conditions**: clean, misleading, correct-context, and irrelevant-context. Its **SC2W** metric counts cases where misleading context flips an otherwise correct answer to wrong.\n\nEvaluate retrieval-augmented agents for selective trust, not only prompt-injection resistance. The proposed **SCOPE** method trains on matched preference pairs balanced across all four conditions so resistance does not come from ignoring useful context.\n\nThe abstract reports reduced susceptibility on popular open models while preserving other-condition accuracy, but provides no numerical effect sizes here. Broader generalization beyond the benchmark's reasoning items remains an open question from the supplied material.", "source": { "name": "arXiv", "url": "https://arxiv.org/abs/2608.06377v1", "published_at": "2026-08-06T00:00:00.000Z" }, "source_class": "blog_post", "content_type": "Paper", "layer": "benchmark", "domains": [ "research" ], "topics": [ "agent-evals", "agent-reliability", "context-engineering" ], "verification": { "status": "needs_review", "label": "Needs Review", "method": "unverified", "verified_at": null }, "uncertainty": [ "Automatically selected from source material; feed7 has not independently tested the claim." ], "connected_context": null, "lifecycle": "New", "published_at": "2026-08-06T00:00:00.000Z", "modified_at": "2026-08-06T00:00:00.000Z", "supersedes": [], "expires_at": null, "formats": { "html": "https://feed7.dev/p/learning-when-to-trust-via-selective-context-preference--91ef3aae9d", "json": "https://feed7.dev/p/learning-when-to-trust-via-selective-context-preference--91ef3aae9d.json", "markdown": "https://feed7.dev/p/learning-when-to-trust-via-selective-context-preference--91ef3aae9d.md" } }, { "schema_version": "1.1", "id": "auto-b3425f8b3f", "slug": "how-cursor-router-chooses-the-right-model-for-the-task-b3425f8b3f", "url": "https://feed7.dev/p/how-cursor-router-chooses-the-right-model-for-the-task-b3425f8b3f", "title": "How Cursor Router chooses the right model for the task", "why_included": "Route models using real task outcomes, corrections, cache costs, confidence thresholds, and an explicit per-turn budget.", "summary": "Cursor Router learns task complexity and model fit from production behavior, showing why agent routing should include correction signals, cache costs, and per-task performance.", "practical_implication": "Builders implementing model routing should learn from real task outcomes, including corrections and task progression, rather than rely only on benchmarks. Include token use, cache misses, model-switching costs, confidence thresholds, and an explicit per-turn budget.", "agent_context": "Cursor routes each turn in two stages: Compass estimates complexity, then a production-derived taxonomy selects among eligible frontier models. **Auto Intelligence costs 68% less than Fable**, while **Auto Balance costs 41% less than Opus 4.8** and reports higher satisfaction.\n\nBuilders implementing model routing should learn from real task outcomes, including corrections and task progression, rather than rely only on benchmarks. Include token use, cache misses, model-switching costs, confidence thresholds, and an explicit per-turn budget.\n\nCursor's satisfaction measure is inferred from subsequent user behavior, so it is a proxy rather than a direct quality score. The reported results come from Cursor traffic and may not transfer to another workload, user population, or pricing mix.", "source": { "name": "Cursor", "url": "https://cursor.com/blog/how-cursor-router-works", "published_at": "2026-08-06T00:00:00.000Z" }, "source_class": "blog_post", "content_type": "Engineering Post", "layer": "agent", "domains": [ "coding", "data" ], "topics": [ "model-selection", "harness-engineering", "agent-reliability" ], "verification": { "status": "official_source", "label": "Official Source", "method": "source_feed", "verified_at": null }, "uncertainty": [], "connected_context": null, "lifecycle": "New", "published_at": "2026-08-06T00:00:00.000Z", "modified_at": "2026-08-06T00:00:00.000Z", "supersedes": [], "expires_at": null, "formats": { "html": "https://feed7.dev/p/how-cursor-router-chooses-the-right-model-for-the-task-b3425f8b3f", "json": "https://feed7.dev/p/how-cursor-router-chooses-the-right-model-for-the-task-b3425f8b3f.json", "markdown": "https://feed7.dev/p/how-cursor-router-chooses-the-right-model-for-the-task-b3425f8b3f.md" } }, { "schema_version": "1.1", "id": "auto-2dd1524bd8", "slug": "pause-workflows-for-approval-with-chat-sdk-2dd1524bd8", "url": "https://feed7.dev/p/pause-workflows-for-approval-with-chat-sdk-2dd1524bd8", "title": "Pause workflows for approval with Chat SDK", "why_included": "Add durable, verified human approval before agents deploy, alter production data, or take other consequential actions.", "summary": "Chat SDK can pause a workflow for a verified human decision and resume after seconds or days, without a custom approvals table, action handler, or polling loop.", "practical_implication": "Use this as a durable gate before an agent deploys, changes production data, or takes another consequential action. Scoped approvers, platform-signature checks, and the returned user identity reduce the custom authorization plumbing.", "agent_context": "Chat SDK’s new **chat/workflow** path adds requestApproval, which posts Approve and Deny controls and suspends a Workflow SDK run. The wait survives deploys and restarts, and Thread state crosses the workflow boundary automatically.\n\nUse this as a durable gate before an agent deploys, changes production data, or takes another consequential action. **Scoped approvers**, platform-signature checks, and the returned user identity reduce the custom authorization plumbing.\n\nThe material covers a binary approval card, timeout handling, and an edited-in-place audit outcome. It does not describe richer review flows, delegation, or how long approval records remain available.", "source": { "name": "Vercel", "url": "https://vercel.com/changelog/chat-sdk-durable-approvals", "published_at": "2026-08-06T00:00:00.000Z" }, "source_class": "blog_post", "content_type": "Engineering Post", "layer": "tools", "domains": [ "coding" ], "topics": [ "agent-sdks", "tool-use", "agent-reliability" ], "verification": { "status": "official_source", "label": "Official Source", "method": "source_feed", "verified_at": null }, "uncertainty": [], "connected_context": null, "lifecycle": "New", "published_at": "2026-08-06T00:00:00.000Z", "modified_at": "2026-08-06T00:00:00.000Z", "supersedes": [], "expires_at": null, "formats": { "html": "https://feed7.dev/p/pause-workflows-for-approval-with-chat-sdk-2dd1524bd8", "json": "https://feed7.dev/p/pause-workflows-for-approval-with-chat-sdk-2dd1524bd8.json", "markdown": "https://feed7.dev/p/pause-workflows-for-approval-with-chat-sdk-2dd1524bd8.md" } }, { "schema_version": "1.1", "id": "auto-a24f8f74a4", "slug": "computer-use-at-the-edge-of-the-statistical-precipice-pi-a24f8f74a4", "url": "https://feed7.dev/p/computer-use-at-the-edge-of-the-statistical-precipice-pi-a24f8f74a4", "title": "Computer Use at the Edge of the Statistical Precipice — Pierluca D'Oro, Programma Labs", "why_included": "Vary task data, appearance, and initial state so agent evals measure adaptation instead of rewarding replayed action scripts.", "summary": "Static computer-use benchmarks can reward memorized action scripts rather than adaptation. Vary task state, verify every generated case, and calculate uncertainty across both actions and environments.", "practical_implication": "For agent evals, vary data, appearance, and initial state; automatically reject invalid combinations; and use privileged verifiers inside a sandbox. DGWorld applies this design across 15 apps, 387 scenarios, and 3.2 million verified configurations.", "agent_context": "A replay agent stores one winning trajectory per task and blindly repeats it. On deterministic OSWorld and MobileWorld-style evaluations, a script **under 1 MB** can match or beat the frontier model that generated its traces, exposing benchmark replayability.\n\nFor agent evals, vary data, appearance, and initial state; automatically reject invalid combinations; and use privileged verifiers inside a sandbox. DGWorld applies this design across **15 apps, 387 scenarios, and 3.2 million verified configurations**.\n\nUncertainty must cover both model actions and environment variation. The talk reports that rollout-only intervals can provide roughly **17–20% coverage** where a correctly structured method approaches the intended 95%, but applying that method requires a benchmark with explicit variation structure.", "source": { "name": "AI Engineer", "url": "https://www.youtube.com/watch?v=CTLa_p6iOiY", "published_at": "2026-08-14T00:00:00.000Z" }, "source_class": "video", "content_type": "Video", "layer": "benchmark", "domains": [], "topics": [ "agent-evals", "benchmark-integrity", "agent-reliability" ], "verification": { "status": "source_linked", "label": "Source Linked", "method": "source_feed", "verified_at": null }, "uncertainty": [], "connected_context": null, "lifecycle": "New", "published_at": "2026-08-14T00:00:00.000Z", "modified_at": "2026-08-14T00:00:00.000Z", "supersedes": [], "expires_at": null, "formats": { "html": "https://feed7.dev/p/computer-use-at-the-edge-of-the-statistical-precipice-pi-a24f8f74a4", "json": "https://feed7.dev/p/computer-use-at-the-edge-of-the-statistical-precipice-pi-a24f8f74a4.json", "markdown": "https://feed7.dev/p/computer-use-at-the-edge-of-the-statistical-precipice-pi-a24f8f74a4.md" } }, { "schema_version": "1.1", "id": "auto-137531afb5", "slug": "apache-maka-137531afb5", "url": "https://feed7.dev/p/apache-maka-137531afb5", "title": "apache/maka", "why_included": "Use its append-only run log and shared runtime to trim context without losing evidence, inspect failures, and recover crashes.", "summary": "Maka gives local coding-agent runs a durable event log, sandbox boundary, recovery path, and shared runtime across desktop, CLI, and evals. It is early, source-only software with changing interfaces.", "practical_implication": "Builders can treat prompts as projections of durable run history: trim old tool output from context without deleting evidence, inspect failures, recover crashes, and compare reproducible experiment cells. Its graph mode also uses isolated Git worktrees for parallel implementation.", "agent_context": "Apache Maka records model messages, tool calls, results, permission decisions, and termination events in an **append-only local log**. Desktop, CLI, TUI, and evaluations execute through one **Runtime Host**, with sandbox approval for file writes and shell commands.\n\nBuilders can treat prompts as projections of durable run history: trim old tool output from context without deleting evidence, inspect failures, recover crashes, and compare reproducible experiment cells. Its graph mode also uses isolated Git worktrees for parallel implementation.\n\nThis is an **Apache Incubator** project with no approved Apache release. The desktop build currently supports **Apple Silicon macOS**; formats and commands may change, Windows is an unsigned preview, and secrets remain plaintext to the local OS account.", "source": { "name": "GitHub", "url": "https://github.com/apache/maka", "published_at": "2026-08-23T00:00:00.000Z" }, "source_class": "tool", "content_type": "GitHub Repo", "layer": "agent", "domains": [ "coding" ], "topics": [ "harness-engineering", "agent-reliability", "sandboxing" ], "verification": { "status": "needs_review", "label": "Needs Review", "method": "unverified", "verified_at": null }, "uncertainty": [ "Automatically selected from source material; feed7 has not independently tested the claim." ], "connected_context": null, "lifecycle": "New", "published_at": "2026-08-23T00:00:00.000Z", "modified_at": "2026-08-23T00:00:00.000Z", "supersedes": [], "expires_at": null, "formats": { "html": "https://feed7.dev/p/apache-maka-137531afb5", "json": "https://feed7.dev/p/apache-maka-137531afb5.json", "markdown": "https://feed7.dev/p/apache-maka-137531afb5.md" } }, { "schema_version": "1.1", "id": "auto-b3f2ae3bdf", "slug": "phantom-gains-auditing-self-improvement-against-a-measur-b3f2ae3bdf", "url": "https://feed7.dev/p/phantom-gains-auditing-self-improvement-against-a-measur-b3f2ae3bdf", "title": "Phantom Gains: Auditing Self-Improvement Against a Measured Null", "why_included": "Add frozen-baseline replicates and a measured null before treating one-decode gains or regressions as real.", "summary": "Per-problem self-improvement claims can arise from inference and evaluation noise. This audit argues every transition statistic needs a measured null from frozen baseline replicates.", "practical_implication": "For agent and model evaluations, do not treat problem-level gains and losses as ground truth from one decode. Measure each statistic’s null with baseline replicates, then use per-problem tests and false-discovery-rate control.", "agent_context": "The study ran three rounds of rank-32 LoRA self-training on Qwen3-8B alongside a frozen control using the same pipeline. It found **seven measurement failures** capable of reversing findings when the control was omitted.\n\nFor agent and model evaluations, do not treat problem-level gains and losses as ground truth from one decode. Measure each statistic’s null with baseline replicates, then use per-problem tests and false-discovery-rate control.\n\nA single greedy decode gave an untrained model an apparent expansion rate of **0.280**, while the replacement exact test detected **nothing on held-out replicates**. Results for problems the base model never solved remained inconclusive, and the method needs more baseline replicates than many studies possess.", "source": { "name": "arXiv", "url": "https://arxiv.org/abs/2608.20290v1", "published_at": "2026-08-20T00:00:00.000Z" }, "source_class": "blog_post", "content_type": "Paper", "layer": "benchmark", "domains": [ "research" ], "topics": [ "benchmark-integrity", "agent-evals", "agent-reliability" ], "verification": { "status": "needs_review", "label": "Needs Review", "method": "unverified", "verified_at": null }, "uncertainty": [ "Automatically selected from source material; feed7 has not independently tested the claim." ], "connected_context": null, "lifecycle": "New", "published_at": "2026-08-20T00:00:00.000Z", "modified_at": "2026-08-20T00:00:00.000Z", "supersedes": [], "expires_at": null, "formats": { "html": "https://feed7.dev/p/phantom-gains-auditing-self-improvement-against-a-measur-b3f2ae3bdf", "json": "https://feed7.dev/p/phantom-gains-auditing-self-improvement-against-a-measur-b3f2ae3bdf.json", "markdown": "https://feed7.dev/p/phantom-gains-auditing-self-improvement-against-a-measur-b3f2ae3bdf.md" } }, { "schema_version": "1.1", "id": "auto-990ea76696", "slug": "break-it-down-pass-it-on-cross-task-skill-transfer-in-ll-990ea76696", "url": "https://feed7.dev/p/break-it-down-pass-it-on-cross-task-skill-transfer-in-ll-990ea76696", "title": "Break It Down, Pass It On: Cross-Task Skill Transfer in LLM Agents", "why_included": "Store small textual procedures, because subtask-level skills transferred better while whole-task skills often hurt performance.", "summary": "Reusable agent skills transferred better when induced from subtasks and stored as text. Whole-task skills often hurt versus no memory, so retrieval alone is not evidence that a memory helps.", "practical_implication": "Build agent memory from small, reusable procedures and prefer textual guidance unless execution requires code. Before retrieval, score candidates for both task specificity and breadth of relevance rather than assuming any prior solution will help.", "agent_context": "The study compares task-level against subtask-level skill induction and text against code formats. **Task-level skills often fell below the no-memory baseline**, while **subtask-level skills improved performance on average**; text transferred better than code.\n\nBuild agent memory from small, reusable procedures and prefer textual guidance unless execution requires code. Before retrieval, score candidates for both task specificity and breadth of relevance rather than assuming any prior solution will help.\n\nNeither specificity nor abstractness predicted outcomes alone. Their combined **skill utility score** correlated with transfer performance and requires only skills plus task descriptions, but the supplied material gives no tasks, models, or effect sizes for judging generality.", "source": { "name": "arXiv", "url": "https://arxiv.org/abs/2608.20274v1", "published_at": "2026-08-20T00:00:00.000Z" }, "source_class": "blog_post", "content_type": "Paper", "layer": "agent", "domains": [], "topics": [ "skills", "agent-memory", "agent-reliability" ], "verification": { "status": "needs_review", "label": "Needs Review", "method": "unverified", "verified_at": null }, "uncertainty": [ "Automatically selected from source material; feed7 has not independently tested the claim." ], "connected_context": null, "lifecycle": "New", "published_at": "2026-08-20T00:00:00.000Z", "modified_at": "2026-08-20T00:00:00.000Z", "supersedes": [], "expires_at": null, "formats": { "html": "https://feed7.dev/p/break-it-down-pass-it-on-cross-task-skill-transfer-in-ll-990ea76696", "json": "https://feed7.dev/p/break-it-down-pass-it-on-cross-task-skill-transfer-in-ll-990ea76696.json", "markdown": "https://feed7.dev/p/break-it-down-pass-it-on-cross-task-skill-transfer-in-ll-990ea76696.md" } }, { "schema_version": "1.1", "id": "auto-105166eadd", "slug": "ai-agents-are-just-distributed-systems-now-salman-munaf--105166eadd", "url": "https://feed7.dev/p/ai-agents-are-just-distributed-systems-now-salman-munaf--105166eadd", "title": "AI Agents Are Just Distributed Systems Now — Salman Munaf, TikTok", "why_included": "Make mutating tools idempotent, persist each step, bound retries and spend, and treat timeouts as unknown outcomes.", "summary": "Once agents mutate external state, timeouts mean unknown outcomes. Builders need idempotent tools, bounded retries, scoped credentials, durable traces, and explicit recovery paths.", "practical_implication": "Design every agent tool like a distributed-system boundary. Persist each step, define the source of truth, treat memory as an invalidatable cache, and bind approvals to the exact action, actor, timestamp, parameters, and expiration.", "agent_context": "A tool timeout does not prove failure: the remote side may already have committed the action. The talk recommends request IDs, **idempotency keys**, status lookups, circuit breakers, compensation operations, and limits on turns, spend, and parallel calls.\n\nDesign every agent tool like a distributed-system boundary. Persist each step, define the source of truth, treat memory as an invalidatable cache, and bind approvals to the exact action, actor, timestamp, parameters, and expiration.\n\nA stronger model can reduce reasoning mistakes but cannot remove network ambiguity, stale state, or adversarial input. Some irreversible actions cannot be truly undone, so compensation and human approval remain domain-specific safeguards.", "source": { "name": "AI Engineer", "url": "https://www.youtube.com/watch?v=hD9-V56FNRI", "published_at": "2026-08-29T00:00:00.000Z" }, "source_class": "video", "content_type": "Video", "layer": "agent", "domains": [ "coding" ], "topics": [ "harness-engineering", "tool-use", "agent-reliability" ], "verification": { "status": "source_linked", "label": "Source Linked", "method": "source_feed", "verified_at": null }, "uncertainty": [], "connected_context": null, "lifecycle": "New", "published_at": "2026-08-29T00:00:00.000Z", "modified_at": "2026-08-29T00:00:00.000Z", "supersedes": [], "expires_at": null, "formats": { "html": "https://feed7.dev/p/ai-agents-are-just-distributed-systems-now-salman-munaf--105166eadd", "json": "https://feed7.dev/p/ai-agents-are-just-distributed-systems-now-salman-munaf--105166eadd.json", "markdown": "https://feed7.dev/p/ai-agents-are-just-distributed-systems-now-salman-munaf--105166eadd.md" } }, { "schema_version": "1.1", "id": "auto-7a4a8849cf", "slug": "how-to-get-your-org-to-adopt-coding-agents-without-shipp-7a4a8849cf", "url": "https://feed7.dev/p/how-to-get-your-org-to-adopt-coding-agents-without-shipp-7a4a8849cf", "title": "How to Get Your Org to Adopt Coding Agents (Without Shipping Garbage) — Eyal Blum, Figma", "why_included": "Write executable checks before implementation, then split agent plans into independently validated, reviewable phases.", "summary": "Figma’s cautious agent adopters expose missing safeguards, not resistance. Use their feedback to improve verification, plans, review size, and the labeling of AI-generated work.", "practical_implication": "Make verification executable before implementation: use TDD, encode repeated checks deterministically, and split plans into independently validated, reviewable phases. Put the reason for the change first, and use skeptical maintainers’ feedback as the safety roadmap.", "agent_context": "Figma reports uneven agent adoption and says experienced engineers may be slowest because they see codebase risks first. One detailed plan turned an estimated **six weeks of coding into about one week**, including review.\n\nMake verification executable before implementation: use TDD, encode repeated checks deterministically, and split plans into independently validated, reviewable phases. Put the reason for the change first, and use skeptical maintainers’ feedback as the safety roadmap.\n\nThe workflow is still evolving and depends on planning and review effort that raw generation metrics can hide. AI also increases communication volume, so clearly separate human-written intent from generated descriptions to protect scarce reviewer attention.", "source": { "name": "AI Engineer", "url": "https://www.youtube.com/watch?v=5Bn0xro2ol8", "published_at": "2026-08-28T00:00:00.000Z" }, "source_class": "video", "content_type": "Video", "layer": "agent", "domains": [ "coding" ], "topics": [ "coding-agents", "agent-reliability", "harness-engineering" ], "verification": { "status": "source_linked", "label": "Source Linked", "method": "source_feed", "verified_at": null }, "uncertainty": [], "connected_context": null, "lifecycle": "New", "published_at": "2026-08-28T00:00:00.000Z", "modified_at": "2026-08-28T00:00:00.000Z", "supersedes": [], "expires_at": null, "formats": { "html": "https://feed7.dev/p/how-to-get-your-org-to-adopt-coding-agents-without-shipp-7a4a8849cf", "json": "https://feed7.dev/p/how-to-get-your-org-to-adopt-coding-agents-without-shipp-7a4a8849cf.json", "markdown": "https://feed7.dev/p/how-to-get-your-org-to-adopt-coding-agents-without-shipp-7a4a8849cf.md" } } ], "conflicting_sources": [], "superseded_claims": [], "corpus_evidence": [ { "title": "User Feedback Provides a Unique Signal that LLMs Can not Detect", "url": "https://arxiv.org/abs/2609.02859v1", "source_name": "arXiv", "published_at": "2026-09-02T17:42:44+00:00", "summary": "User feedback helps models repair targeted faults, but LLM judges often miss those repairs. Agent evals should retain human feedback and avoid treating model preference as ground truth." }, { "title": "The Implications of Linguistic Illegibility for LLM Security", "url": "https://arxiv.org/abs/2609.02852v1", "source_name": "arXiv", "published_at": "2026-09-02T17:37:22+00:00", "summary": "Language-based monitoring cannot fully reveal an agent’s internal computation, the paper argues. Secure agent runtimes still need isolation and data-flow controls independent of self-reporting." }, { "title": "Large Language Models (LLMs) for Telecom Root Cause Analysis (RCA): A Structured Reasoning Framework for Evidence-Grounded Diagnosis", "url": "https://arxiv.org/abs/2609.02805v1", "source_name": "arXiv", "published_at": "2026-09-02T16:43:22+00:00", "summary": "A telecom diagnosis pipeline structures telemetry before forcing a decision path and evidence-grounded explanation. The pattern is relevant to agents reasoning over heterogeneous operational data." }, { "title": "Dutch Books for Language Models", "url": "https://arxiv.org/abs/2609.02797v1", "source_name": "arXiv", "published_at": "2026-09-02T16:31:47+00:00", "summary": "A label-free Dutch-book test finds internally inconsistent probabilities from language models, especially when prompts add logical complexity or irrelevant context." }, { "title": "SafeEvolve: Harness-Policy Co-Evolution from Agent Experience for Safety Alignment", "url": "https://arxiv.org/abs/2609.02786v1", "source_name": "arXiv", "published_at": "2026-09-02T16:19:54+00:00", "summary": "SafeEvolve turns completed agent trajectories into reversible harness updates and policy training, linking runtime safeguards with learned multi-step behavior." }, { "title": "Beyond the Lethal Trifecta: Agentic Commerce on the Open Internet — David Levine, Kiduna Club", "url": "https://www.youtube.com/watch?v=tE2z8-hqoLY", "source_name": "AI Engineer", "published_at": "2026-09-01T20:30:27+00:00", "summary": "This talk proposes legally registered agent organizations, scoped JWT authority, and blockchain audit trails for open-internet commerce; it is an architecture proposal, not validation." }, { "title": "Agent Spending Without Controls — Rodrigo Coelho & Pranav Maheshwari, Edge & Node", "url": "https://www.youtube.com/watch?v=ZyGMqdIpPoE", "source_name": "AI Engineer", "published_at": "2026-09-01T19:30:09+00:00", "summary": "Paid agent tools turn a skill file into spending authority. Builders need budgets, transaction logs, and counterparty screening before letting agents purchase data or services unattended." }, { "title": "x402 isn’t good (yet) — Jan Curn, Apify", "url": "https://www.youtube.com/watch?v=h6mi88VrPtQ", "source_name": "AI Engineer", "published_at": "2026-09-01T19:00:09+00:00", "summary": "x402 servers can perform work before payment settlement, leaving a double-spend window. Builders should settle first or accept explicit counterparty risk until stronger schemes mature." }, { "title": "Your Agent Just Authorized What?! — Jay Mok & Ben Coumes, Paypal", "url": "https://www.youtube.com/watch?v=vGn6N4-bxBY", "source_name": "AI Engineer", "published_at": "2026-09-01T18:30:06+00:00", "summary": "Agent authorization should scale with stakes and reversibility: logs may cover coding changes, while open-ended payments need scoped, time-bound, independently verifiable mandates." }, { "title": "When AI Agents Pay and Sellers Monetize: Building x402 Apps on AWS — Anil Nadiminti, AWS", "url": "https://www.youtube.com/watch?v=qTZirYu9pr0", "source_name": "AI Engineer", "published_at": "2026-09-01T18:00:14+00:00", "summary": "AWS is separating agent payment policy from model execution: AgentCore handles wallets and limits, while WAF meters bot access. The useful pattern is deterministic spend control at the edge." }, { "title": "CordisBench: Can Language Models Reason About Component Lifecycles in Dynamic Agent Harnesses?", "url": "https://arxiv.org/abs/2609.01600v1", "source_name": "arXiv", "published_at": "2026-09-01T17:59:13+00:00", "summary": "CordisBench finds that models lose reliability as harness lifecycle interactions grow, while deterministic execution semantics can replace costly reasoning on controlled cases." }, { "title": "StudentSim: Training LLM-based Student Simulators", "url": "https://arxiv.org/abs/2609.01591v1", "source_name": "arXiv", "published_at": "2026-09-01T17:55:10+00:00", "summary": "StudentSim turns sparse user histories into individualized simulators that model both current behavior and response to guidance, a pattern for testing adaptive agents before live deployment." }, { "title": "From Production Traffic to Post-Training: Building a Self-Hosted LLM That Covers the Corporate Request Mix", "url": "https://arxiv.org/abs/2609.01572v1", "source_name": "arXiv", "published_at": "2026-09-01T17:39:26+00:00", "summary": "A production-derived post-training recipe consolidated more than 200 internal apps onto one self-hosted model by training separate experts for distinct quality gaps, then merging them." }, { "title": "Selective Agent Guidance via Entropy: Learning Autonomous Policies from Imperfect VLM Teachers", "url": "https://arxiv.org/abs/2609.01567v1", "source_name": "arXiv", "published_at": "2026-09-01T17:33:41+00:00", "summary": "SAGE uses an expensive VLM only when a learner is uncertain, tests its advice against environment rewards, and distills useful guidance into a policy that needs no VLM at runtime." }, { "title": "Why Your AI Agent Needs a Wallet: USDC and Nanopayments — Harshal Bhangale, Circle", "url": "https://www.youtube.com/watch?v=xKzU_3riL6s", "source_name": "AI Engineer", "published_at": "2026-09-01T17:30:10+00:00", "summary": "A wallet-equipped agent crossed paywalls and completed paid email and phone actions under a spending cap. The engineering lesson is to enforce budgets in the wallet, not in prompts." }, { "title": "Teaching agents to pay — Anna Spysz, Stripe", "url": "https://www.youtube.com/watch?v=A-zeQiYkmXk", "source_name": "AI Engineer", "published_at": "2026-09-01T16:30:06+00:00", "summary": "Agent checkout needs more than payment tools: enforce user-set limits outside the agent, expose structured merchant data, honor cancellation, and log every decision." }, { "title": "Claude Fable 5.1 now available on AI Gateway", "url": "https://vercel.com/changelog/claude-fable-5-1-now-available-on-ai-gateway", "source_name": "Vercel", "published_at": "2026-09-01T00:00:00+00:00", "summary": "Claude Fable 5.1 reaches Vercel AI Gateway with ordered fallbacks for classifier refusals, but its 30-day retention policy rules out zero-data-retention workloads." }, { "title": "DIASENTINEL: An Auditable Multi-Agent System for Guideline-Grounded Diabetes Risk Screening", "url": "https://arxiv.org/abs/2608.31128v1", "source_name": "arXiv", "published_at": "2026-08-31T17:40:43+00:00", "summary": "DIASENTINEL combines deterministic extraction, guideline retrieval, risk prediction, and hybrid verification on-premise. It is a useful architecture reference for auditable agents handling sensitive data." }, { "title": "BLOOM-WILT: Logit Tilting for Behaviour Elicitation in Automated LLM Auditing", "url": "https://arxiv.org/abs/2608.31105v1", "source_name": "arXiv", "published_at": "2026-08-31T17:10:59+00:00", "summary": "BLOOM-WILT makes rare model behaviors easier to surface by adapting both the auditor’s strategy and target decoding. Its results also show that elicitation method can reverse safety rankings." }, { "title": "S3Gym: Can LLMs Turn Self-Testing and Self-Judging into Self-Improvement?", "url": "https://arxiv.org/abs/2608.31100v1", "source_name": "arXiv", "published_at": "2026-08-31T17:05:41+00:00", "summary": "S3Gym tests whether agents can explore, judge outcomes, and reuse experience. Raw history, compressed memory, and training each help in some tasks, but none is a reliable default." }, { "title": "Tribal Dungeons of Global Shipping: AI Agents at Global Scale — Dmitry Buykin, Maersk", "url": "https://www.youtube.com/watch?v=dQ-_i1tZiws", "source_name": "AI Engineer", "published_at": "2026-08-29T17:30:21+00:00", "summary": "Maersk’s production agents depend less on a clever loop than on executable SOPs, bounded tools, replayable traces, and a correction system shared by experts and engineers." }, { "title": "Agents Are Where Microservices Were in 2015 — Roberto Milev & Uday Kanagala, Navan", "url": "https://www.youtube.com/watch?v=32nrHU6zHU8", "source_name": "AI Engineer", "published_at": "2026-08-29T16:30:28+00:00", "summary": "Navan treats production agents as stateful services needing persistence, composable skills, traces, authorization gates, and trajectory-based tests before multi-agent complexity." }, { "title": "The Half Life of Agent Infrastructure — Ben Kus, Box", "url": "https://www.youtube.com/watch?v=sM1iYgz93HI", "source_name": "AI Engineer", "published_at": "2026-08-29T14:30:22+00:00", "summary": "Agent architectures are expiring quickly. Keep model, search, and orchestration choices replaceable, and evaluate platforms by how well they handle repeated change." }, { "title": "Which AI startups actually land enterprise contracts? — Brian Lewis, Millennium", "url": "https://www.youtube.com/watch?v=7A65O-0lvKE", "source_name": "AI Engineer", "published_at": "2026-08-29T14:00:06+00:00", "summary": "Enterprise AI contracts are won on security, controls, integration, and support as much as model capability. Builders should make those operational surfaces part of the product early." }, { "title": "Your Code Has Bugs. Lean4 Has Proofs: Formal Verification for Engineers — Varun Pant, AWS", "url": "https://www.youtube.com/watch?v=lRa9sPaMyy4", "source_name": "AI Engineer", "published_at": "2026-08-28T18:00:17+00:00", "summary": "Formal verification gives agent-generated code a machine-checkable contract beyond tests. The hard part shifts upstream: humans must define and validate what correct means." }, { "title": "How to Generate Mergeable Code with a Context Engine — Peter Werry, Unblocked", "url": "https://www.youtube.com/watch?v=qdAkxLoYNI8", "source_name": "AI Engineer", "published_at": "2026-08-27T18:00:23+00:00", "summary": "Agents often stop after finding plausible code context. Werry argues for task-specific retrieval that also surfaces decisions, conventions, PR history, and expertise before planning or review." }, { "title": "RedEvoAgent: Automatic Red-Teaming Agent with Experience-Driven Skill Evolution", "url": "https://arxiv.org/abs/2608.27439v1", "source_name": "arXiv", "published_at": "2026-08-27T17:55:33+00:00", "summary": "RedEvoAgent turns prior jailbreak trajectories into a compact attack skill, then keeps only validated improvements. It offers a more interpretable way to probe tool-using agents for unsafe actions." }, { "title": "Persona-Execution Separation: An Architecture Pattern for Evolving LLM Agents under Execution Audit", "url": "https://arxiv.org/abs/2608.27427v1", "source_name": "arXiv", "published_at": "2026-08-27T17:50:07+00:00", "summary": "Persona-Execution Separation isolates mutable agent behavior from audited, stateful work through a governed bridge. It is an architecture option for multi-user agents under compliance constraints." }, { "title": "How Anthropic Builds: Lessons from Labs — Mike Krieger, Anthropic", "url": "https://www.youtube.com/watch?v=qqrk7CtkuIw", "source_name": "AI Engineer", "published_at": "2026-08-27T16:00:06+00:00", "summary": "Mike Krieger describes shifting from stepwise prompting to goal-level delegation, backed by verification, observability, feature flags, and frequent decisions to continue or stop experiments." }, { "title": "TraceML: An Empirical Analysis of Human-Agent Planning in Machine Learning Development", "url": "https://arxiv.org/abs/2608.26086v1", "source_name": "arXiv", "published_at": "2026-08-26T17:50:13+00:00", "summary": "TraceML exposes why coding agents stall on long ML work: they repeat narrow optimization loops instead of alternating work types, revisiting discarded ideas, and pivoting like experts." }, { "title": "Trace Integrity for LLM Data Agents: A Vision for Auditable Structured Reasoning in Real-World Systems", "url": "https://arxiv.org/abs/2608.26036v1", "source_name": "arXiv", "published_at": "2026-08-26T17:15:24+00:00", "summary": "Correct answers can hide invalid computation. Trace Integrity proposes executable, replayable contracts for data agents and reports 45.8–59.1% correct-answer/invalid-trace rates across three SQL modes." }, { "title": "Building GTM AI Agents: Lessons from Deploying to 6,000 Users — Sait Izmit, Snowflake", "url": "https://www.youtube.com/watch?v=DrTdD-ttjCY", "source_name": "AI Engineer", "published_at": "2026-08-26T15:00:06+00:00", "summary": "Snowflake’s rollout favors narrow, high-accuracy coverage, staged adoption, and log-driven iteration over connecting every data source before launch." }, { "title": "AI in GTM at Notion — Flora Liu", "url": "https://www.youtube.com/watch?v=L4I7WgiEquo", "source_name": "AI Engineer", "published_at": "2026-08-26T14:00:05+00:00", "summary": "Notion treats GTM automation as a shared context system for humans and agents, not an AI layer over disconnected tools. The key design choice is keeping risky customer actions human-approved." }, { "title": "GTM Engineering: The Technical Bits — Everett Berry, Clay", "url": "https://www.youtube.com/watch?v=UhCY231d0FQ", "source_name": "AI Engineer", "published_at": "2026-08-26T13:30:31+00:00", "summary": "Clay frames GTM agents as a data and orchestration problem: resolve entities, evaluate providers, isolate agent-written state, and account for delayed syncs before automating decisions." }, { "title": "The Missing Layer in Agentic AI — Giedrius Šteimantas, Oxylabs", "url": "https://www.youtube.com/watch?v=XsvUhpnHepE", "source_name": "AI Engineer", "published_at": "2026-08-26T07:00:06+00:00", "summary": "Web agents should validate fetched content before spending model tokens on it and reserve browser automation for interactive steps. Explicit failures beat silently passing CAPTCHAs or blocked HTML downstream." }, { "title": "Inside 847 Production Clinical AI Notes — Sebastian Fox, Composo", "url": "https://www.youtube.com/watch?v=yqF6XhzbWBk", "source_name": "AI Engineer", "published_at": "2026-08-22T17:00:32+00:00", "summary": "Plausible outputs can hide consequential omissions that generic LLM judges miss. Production evals need real failure discovery and retrieved expert judgments, not a frozen rubric alone." }, { "title": "Agent Frameworks Considered Harmful — Rémi Louf, .txt", "url": "https://www.youtube.com/watch?v=KHudyx5wW3U", "source_name": "AI Engineer", "published_at": "2026-08-22T16:30:39+00:00", "summary": "Reliable background agents need runtime primitives more than elaborate framework abstractions: durable events, queues, versioned inputs, causal traces, diffs, and replay." }, { "title": "What If Your Chip Design Team Moved Like a Single Body? — Abduallah Mohamed, AIDAChip", "url": "https://www.youtube.com/watch?v=0I6aoPSRzVc", "source_name": "AI Engineer", "published_at": "2026-08-22T15:00:25+00:00", "summary": "Tool-by-tool restrictions failed to contain a chip-design agent. Enforce permissions at the substrate, isolate files by role, and propagate approved spec changes from one source of truth." } ] }