{ "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" } } ], "conflicting_sources": [], "superseded_claims": [], "corpus_evidence": [ { "title": "MCP Tasks (async): Why Aren't Any Agents Supporting Them? — Cornelia Davis, Temporal", "url": "https://www.youtube.com/watch?v=s4r6nk5WsZw", "source_name": "AI Engineer", "published_at": "2026-08-02T20:00:06+00:00", "summary": "MCP Tasks aim to make long-running tool calls durable across disconnects and crashes. The proposed V2 removes session-heavy state, but polling scale and implementation complexity remain unresolved." }, { "title": "When Will The Benchmaxxing Plague End? — Nick Heiner, Surge AI", "url": "https://www.youtube.com/watch?v=-npY6XjM8CQ", "source_name": "AI Engineer", "published_at": "2026-08-02T16:30:06+00:00", "summary": "Nick Heiner argues that leaderboard gains can diverge from useful agent behavior through contamination, weak verifiers, reward hacking, and test conditions that users cannot inspect." }, { "title": "Teaching AI to Find Real Vulnerabilities — David Brumley, Bugcrowd", "url": "https://www.youtube.com/watch?v=ZFxh7sqbUZo", "source_name": "AI Engineer", "published_at": "2026-08-01T00:30:06+00:00", "summary": "Cybersecurity agents need deterministic exploit oracles and tasks that reward finding distinct vulnerabilities, not self-reported wins or one crash. A capability ladder makes partial progress measurable." }, { "title": "Rethinking Environments for Long-Horizon Work — Rayan Garg, Theta Software", "url": "https://www.youtube.com/watch?v=2aS7aKoXn64", "source_name": "AI Engineer", "published_at": "2026-08-01T00:00:06+00:00", "summary": "Human task duration is a weak proxy for agent difficulty when agents use different shortcuts and tools. Long-horizon evals need environment complexity, queryable trajectories, and judges that inspect final state." }, { "title": "What's Next After RLHF? — Diogo Almeida, TypeSafe AI", "url": "https://www.youtube.com/watch?v=cJ0EOzey--o", "source_name": "AI Engineer", "published_at": "2026-07-31T23:30:06+00:00", "summary": "RLHF can make agents persuasive assistants without making them dependable autonomous decision-makers. Builders should separate human-pleasing interaction from calibrated automation and keep stakes bounded." }, { "title": "Learning on the Job: The Future of Post-Training — Raymond Feng, Applied Compute", "url": "https://www.youtube.com/watch?v=k35LeKZEhiE", "source_name": "AI Engineer", "published_at": "2026-07-31T22:30:06+00:00", "summary": "Training on a production harness could adapt an agent to real workflows, but non-replayable interactions break common RL assumptions and make feedback harder to turn into updates." }, { "title": "Data and Environment Curation for Post-Training LLMs — Mahesh Sathiamoorthy, Bespoke Labs", "url": "https://www.youtube.com/watch?v=ewtOo0scUh0", "source_name": "AI Engineer", "published_at": "2026-07-31T22:00:06+00:00", "summary": "Post-training gains depend heavily on task selection, rollout quality, and environment design. For many enterprise agents, curated SFT may deliver most of the value before costly RL." }, { "title": "Scaling to Long Horizons — Ross Taylor & Chengxi Taylor, General Reasoning", "url": "https://www.youtube.com/watch?v=2bvtay8wGYI", "source_name": "AI Engineer", "published_at": "2026-07-31T21:30:06+00:00", "summary": "Long-running agents strain context, reward design, and GPU scheduling at once. Compaction and value models can help, but each introduces another optimization trade-off." }, { "title": "Verifiable Environments for AI in Biology — Kenny Workman, LatchBio", "url": "https://www.youtube.com/watch?v=3ZMUiFaQ3qg", "source_name": "AI Engineer", "published_at": "2026-07-31T20:00:35+00:00", "summary": "Biology agents need evaluators that verify analysis of large experimental datasets, not recall. LatchBio found human review essential because valid scientific paths can defeat brittle graders." }, { "title": "TokTier: Exact Stateful Tokenization for Agentic LLM Serving", "url": "https://arxiv.org/abs/2607.29678v1", "source_name": "arXiv", "published_at": "2026-07-31T17:56:30+00:00", "summary": "TokTier targets a hidden agent-serving bottleneck: repeatedly tokenizing long transcripts after small tool results. Its stateful repair cut median vLLM time to first token by 16–34% in reported tests." }, { "title": "AgentHPOBench: A Benchmark For Evaluating LLM Agents as Sequential Hyperparameter Optimizers", "url": "https://arxiv.org/abs/2607.29626v1", "source_name": "arXiv", "published_at": "2026-07-31T16:58:00+00:00", "summary": "AgentHPOBench tests whether agents can learn from experiment history, not merely produce code. Its results expose weaknesses in sustained refinement and log diagnosis across sequential ML runs." }, { "title": "When Does On-Policy Interaction Help? Representational Tradeoffs in Value-Based Imitation Learning", "url": "https://arxiv.org/abs/2607.29617v1", "source_name": "arXiv", "published_at": "2026-07-31T16:52:47+00:00", "summary": "OVI suggests interactive expert feedback can let a smaller learner imitate values without representing the expert's full policy. The result matters to agent distillation, but relies on specific assumptions." }, { "title": "Reinforcement Learning without Verifiable Rewards — Will Brown, Prime Intellect", "url": "https://www.youtube.com/watch?v=AQv3qRCG6Gw", "source_name": "AI Engineer", "published_at": "2026-07-31T15:00:06+00:00", "summary": "For agent work without deterministic answers, mine production traces into tasks, build controllable simulators, and use scaled judges to derive rubrics while watching for reward hacking." }, { "title": "AISPA: User-Centric System Prompt Auditing for Large Language Model Applications", "url": "https://arxiv.org/abs/2607.28617v1", "source_name": "arXiv", "published_at": "2026-07-30T17:58:58+00:00", "summary": "AISPA turns system-prompt review into an eight-dimension audit. Its survey suggests builders should test prompts for user protection and conflicting instructions, not merely check that safeguards exist." }, { "title": "OSReward: Instituting Standardized Evaluation for Cross-Platform Computer-Use Reward Models", "url": "https://arxiv.org/abs/2607.28609v1", "source_name": "arXiv", "published_at": "2026-07-30T17:57:41+00:00", "summary": "OSReward finds that VLM judges often approve failed computer-use runs. Its benchmark and open reward models offer a more grounded way to evaluate trajectories without paying frontier-model costs." }, { "title": "Sample More, Reflect Less: Self-Refine and Reflexion Lose to Repeated Sampling at Equal Token Cost, from 1.5B to 7B", "url": "https://arxiv.org/abs/2607.28576v1", "source_name": "arXiv", "published_at": "2026-07-30T17:38:23+00:00", "summary": "At equal generated-token cost, repeated sampling matched or beat seven reflection, critique, selection, and debate methods. Agent evals should budget every generated token, not compare against one-shot baselines." }, { "title": "First Steps Toward Automated AI Research — Richard Socher, CEO Recursive AI", "url": "https://www.youtube.com/watch?v=pWXUkLP9uWM", "source_name": "AI Engineer", "published_at": "2026-07-30T16:59:37+00:00", "summary": "Socher’s automated-research design combines prior knowledge, measurement data, simulation, physical experiments, and agent orchestration, with early demonstrations in training and CUDA optimization." }, { "title": "Build for the Memo, Not the Demo — Shawn Chan, China Resources Holdings", "url": "https://www.youtube.com/watch?v=tJFjeMBKbIY", "source_name": "AI Engineer", "published_at": "2026-07-30T02:00:06+00:00", "summary": "Finance agents need claim-level provenance, explicit uncertainty, consistency checks, surfaced contradictions, and logged approval. Fluent output without those controls will not survive diligence." }, { "title": "Wearing the Agent: From Group Chats to Glasses — Sai Krishna Rallabandi", "url": "https://www.youtube.com/watch?v=s67bE2Ur3bY", "source_name": "AI Engineer", "published_at": "2026-07-29T22:58:19+00:00", "summary": "Shared agents need a different harness: action-boundary security, selective group memory, per-user privacy, and routing that decides both who receives information and when the agent should speak." }, { "title": "How Kepler Built Verifiable AI for Financial Services — Vinoo Ganesh", "url": "https://www.youtube.com/watch?v=Tt2kX2sgQio", "source_name": "AI Engineer", "published_at": "2026-07-29T21:00:06+00:00", "summary": "Kepler’s pattern keeps LLMs away from numeric mutation: agents choose sources and steps, while deterministic systems persist, calculate, and reject unverifiable values." }, { "title": "Why Off-the-Shelf AI Doesn't Understand Money — Udi Menkes, Intuit", "url": "https://www.youtube.com/watch?v=Owb8g3yDyzo", "source_name": "AI Engineer", "published_at": "2026-07-29T20:00:06+00:00", "summary": "Intuit argues that domain context alone does not create experience. Its approach learns from verified state-action-outcome histories, using frontier models only to propose candidates." }, { "title": "SimulationMaxxing: How we ship agents 20× faster — Aman Gupta (Nubank) + Shreya Rajpal (Snowglobe)", "url": "https://www.youtube.com/watch?v=KMR_RBoCa4M", "source_name": "AI Engineer", "published_at": "2026-07-29T19:00:06+00:00", "summary": "Nubank uses simulated multi-turn traces to evaluate agent changes before production, shortening release cycles while checking simulation results against real data and human review." }, { "title": "How Forward Deployed Engineering is done at Factory — Eno Reyes", "url": "https://www.youtube.com/watch?v=wpOA-UXynoM", "source_name": "AI Engineer", "published_at": "2026-07-28T22:00:06+00:00", "summary": "Factory’s deployed-engineering model treats agent adoption as workflow design: instrument the path from signal to deploy, build validators, and tie autonomy to measurable business outcomes." }, { "title": "AI tools for Forward Deployed Engineering — Vasuman Moza, Varick Agents", "url": "https://www.youtube.com/watch?v=l0FLhNqBOic", "source_name": "AI Engineer", "published_at": "2026-07-28T21:00:06+00:00", "summary": "Varick treats enterprise agents as process-reengineering systems: capture how work really happens, encode that context, then automate only the steps whose risk permits it." }, { "title": "Desktop-Delta Bench: Do Computer-Use Models Understand Desktop GUI Transitions?", "url": "https://arxiv.org/abs/2607.26041v1", "source_name": "arXiv", "published_at": "2026-07-28T17:49:51+00:00", "summary": "Desktop-Delta Bench tests whether computer-use models can verify what changed after an action, exposing a reliability gap hidden by end-task and single-frame scores." }, { "title": "Does Runtime Topology Context Improve LLM-Generated Kubernetes Security Patches?", "url": "https://arxiv.org/abs/2607.25995v1", "source_name": "arXiv", "published_at": "2026-07-28T17:12:12+00:00", "summary": "Kubernetes security patches improved sharply when the model received live service dependencies, showing that scanner findings alone omit context needed to avoid breaking workloads." }, { "title": "AI Agents for Performance: Ship Faster, Pay Less — Rajat Shah, Netflix", "url": "https://www.youtube.com/watch?v=CgsWxRUY5Eo", "source_name": "AI Engineer", "published_at": "2026-07-28T00:59:04+00:00", "summary": "Netflix’s performance-agent playbook feeds production profiles and exact deployed code to agents, then relies on canaries and engineers to validate proposed optimizations." }, { "title": "The Physics of Multi-Turn Long-Horizon Planning: From Pre-training to Post-training via Single- and Multi-Teacher On-Policy Agentic Distillation", "url": "https://arxiv.org/abs/2607.24720v1", "source_name": "arXiv", "published_at": "2026-07-27T17:55:03+00:00", "summary": "Controlled experiments suggest long-horizon agent planning depends on explicit state transitions, some compositional trajectories, and compatible teacher patterns—not atomic skills alone." }, { "title": "ERUnderstand: Evaluating Vision-Language Models on Structured ER Diagrams", "url": "https://arxiv.org/abs/2607.24707v1", "source_name": "arXiv", "published_at": "2026-07-27T17:46:43+00:00", "summary": "ERUnderstand shows vision-language models can recover common ERD elements but often miss rarer schema constructs, so image-to-schema agent workflows still need structural validation." }, { "title": "WebSocket support for OpenAI Responses API live on AI Gateway", "url": "https://vercel.com/changelog/websocket-support-for-openai-responses-api-live-on-ai-gateway", "source_name": "Vercel", "published_at": "2026-07-27T00:01:00+00:00", "summary": "Vercel’s gateway now supports persistent WebSocket sessions for the Responses API, reducing repeated context transfer during long, tool-heavy agent runs." }, { "title": "DeepSWE: A Contamination-Resistant Coding Benchmark — James Shi, Datacurve", "url": "https://www.youtube.com/watch?v=Yk87oUPVaxU", "source_name": "AI Engineer", "published_at": "2026-07-26T18:10:56+00:00", "summary": "DeepSWE uses original long-horizon tasks to reduce contamination and expose coding-agent behaviors hidden by saturated PR-mined suites. Its current task mix still underrepresents some everyday work." }, { "title": "Evals-Driven Development for a Mental Health AI Coach — Akele Reed & Dave Revere, SonderMind", "url": "https://www.youtube.com/watch?v=O72p-rBb2bA", "source_name": "AI Engineer", "published_at": "2026-07-25T23:00:36+00:00", "summary": "SonderMind turns clinician-reviewed failures into release-gating evals, keeping mental-health guardrails modular and testing false positives, false negatives, category, and timing." }, { "title": "Loop Engineering from First Principles — Kyle Mistele, HumanLayer", "url": "https://www.youtube.com/watch?v=xIt_mTQp6mY", "source_name": "AI Engineer", "published_at": "2026-07-25T20:41:40+00:00", "summary": "Replace unbounded coding-agent loops with measured, incremental changes: sense code state, choose one bounded task, open a reviewable PR, and pause until a human responds." }, { "title": "From Agent Traces to Agent Simulations — Rustem Feyzkhanov, Snorkel AI", "url": "https://www.youtube.com/watch?v=Ib5t2RLtxvM", "source_name": "AI Engineer", "published_at": "2026-07-25T01:00:02+00:00", "summary": "Reconstructing production traces as replayable environments lets teams compare agent stacks under fixed conditions and gate releases on task outcomes, cost, latency, and retries." }, { "title": "Building Closed-Loop Evals for a Multimodal Agent at Scale — Soumya Gupta & Jai Chopra, Uber", "url": "https://www.youtube.com/watch?v=31GUkCBD-Uc", "source_name": "AI Engineer", "published_at": "2026-07-24T22:00:25+00:00", "summary": "Uber’s image-editing agent uses routing, iterative QA, golden-set gates, and production feedback to avoid costly edits, hallucinated food, and quality regressions." }, { "title": "From Signal to PR: Anatomy of a Self-Improving Agent — Jason Lopatecki, Arize", "url": "https://www.youtube.com/watch?v=9HbzAWnKbo4", "source_name": "AI Engineer", "published_at": "2026-07-24T20:15:32+00:00", "summary": "Agents can turn production traces, evals, repository context, and observability skills into investigated issues or draft fixes. Human review remains more important as changes grow." }, { "title": "The Future of Evals: From LLM as a Judge to Agent as a Judge — Aparna Dhinakaran, Arize AI", "url": "https://www.youtube.com/watch?v=q2JrUKBMf0w", "source_name": "AI Engineer", "published_at": "2026-07-24T20:00:06+00:00", "summary": "Fixed-rubric judges miss failures that emerge across long, variable agent trajectories. Arize argues for adding agent-based analysis while retaining deterministic and LLM-judge evals." } ] }