Autonomous Agents with Reliable Outcomes

Covers TypeSafe AI's 'Jev' fast/cheap classifier model and nine use cases (routing, guardrails, tool-call gating, inbox triage, reranking, evals, bulk labeling, real-time decisions, confidence gating)…

Distinguishes technical failures from semantic failures (malformed output, hallucination, wrong tool call) unique to probabilistic systems; covers fallback chains, circuit breakers, and idempotency re…

A 5-layer YouTube research agent (tools, memory, context profiles, scoring rubric, gated operating modes) produces a 'rejection log' citing the exact line in the user's content-strategy file that disq…

Matt Pocock (creator of the 'grill-me' skill) shares concrete techniques: having the agent interview the user before starting work to front-load context, 'strategic vs. tactical programming' as the ne…

Linear's Datadog/Sentry-triggered bug-autofix loop fixed 300+ bugs in 30 days; Anthropic's 'Claude maintains apps' Slack-based routine opened 388 PRs across repos with 180 merged (45% merge rate) via …

Claire Vo migrated ~7 OpenClaw agents to Grok Bot via a secrets-free 'brain transplant' export/import, preserving personality/context. Key pattern: approval gates (e.g. a refund agent needs human conf…

Solo founder Ryan Carson runs 10-15 Devin threads at once organized into P0/P1/P2/Bugs folders, treating each thread like a direct report (clear goal, right priority, no unnecessary hand-holding), plu…

Addy Osmani's field guide to brownfield agentic engineering: a green/yellow/red risk-zone map (a human draws it, never the agent), durable 'comprehension memo' research artifacts so agents don't repea…

Podcast interview on why formal methods like TLA+ matter for reliable software, and whether AI makes formal verification practical at mainstream scale for the first time.

Covers how LLM agent memory (context/KV cache) drives up cost as conversations grow, and techniques to control it.

Explains GraphRAG — using a knowledge graph rather than flat vector search — for answering questions whose answers are scattered across many documents rather than contained in one passage.

Companion piece to ByteByteGo's technical deep-dive, based on interviews with the same OpenAI engineers, covering model tiering, load balancing, WebSocket migration, prompt caching, and OpenAI's inter…

Larridin's CTO walks through a concrete 6-step spec-driven workflow for delegating complex work to AI reliably: spike unknowns first, promote the spike to a commented reference implementation, write a…

Deep technical walkthrough of OpenAI's harness/API/inference optimization stack for Codex and ChatGPT Work at ~10M users: persistent WebSockets replacing per-call HTTPS, stable prompt-prefix caching, …

ByteByteGo synthesizes the 'Twelve-Factor Agents' movement plus lessons from Anthropic, Cognition, and Intercom into four production-reliability practices, with real failure case studies (Cursor's fab…

Addy Osmani details Claude Code's four loop types (agentic, goal-based /goal, time-based /loop, proactive) with concrete syntax examples, verification-skill patterns, and a cautionary story about near…

ByteByteGo compares three retrieval approaches for giving LLMs access to organizational data: standard RAG, Graph RAG (using knowledge graphs), and Agentic RAG (where agents decide what to retrieve). …

ByteByteGo examines enterprise AI customer support in the travel industry, specifically addressing why certain support cases always resist automation regardless of model quality. The piece exposes the…

This How I AI episode covers the concept of an 'agent harness' — the infrastructure layer that wraps an AI agent for production: retries, monitoring, logging, and state management. Alongside a GPT-5.6…

ByteByteGo explores how AI agents handle memory across long-running tasks — the architectural constraints that force the problem, the tradeoffs between in-context, external, and hybrid memory, and the…

Anwar Haneef, GM & Head of Ecosystem at Canva, shares five learnings from building Canva's platform specifically for AI agents — how agents consume APIs, what product decisions change when agents are …

ByteByteGo walks through the agent loop structure: how an agent decides what to do on each turn, what scaffolding holds it together, and critically — when and how an agent should stop. Focuses on the …

Addy Osmani proposes a framework of autonomy levels for agentic engineering — essentially a spectrum from fully supervised to fully autonomous, with guidelines for when each level is appropriate. The …

ByteByteGo interviews Marco Casalaina, VP of Products for Microsoft Core AI, on what it actually takes to ship AI agents at enterprise scale. The piece covers the infrastructure, coordination, and rel…

Pragmatic Engineer deepdive investigates 'loop engineering' — a term spreading through engineering circles. Gergely finds triggers, cron jobs, and agent loops at its core, notes the presence of 'AI sl…

ByteByteGo compares the three emerging agent communication protocols: MCP (Model Context Protocol, Anthropic), A2A (Agent-to-Agent, Google), and ACP (Agent Communication Protocol). The piece argues co…

ByteByteGo shares an ex-Meta L8 engineer's (senior staff / principal level) personal agentic engineering setup — how they structure their work with AI agents to be productive and avoid common pitfalls…

The same How I AI roundup includes an episode on how Claude Mythos surfaced a bug that had been lurking in a codebase for 15 years — an illustration of AI's ability to do deep, non-obvious static anal…

Weekly How I AI roundup includes an episode on writing AI agent loops in Claude Code and Codex — practical guidance on structuring agentic workflows that actually complete tasks reliably.

Claire Vo (ChatPRD) walks through Codex /goal in depth with real examples: a 5h45m autonomous run eliminating hundreds of Sentry errors, and a 4-hour job that cleaned 3,900 emails down to 68. The six-…

Michael Bolin (OpenAI Codex tech lead) describes his personal AI-assisted engineering workflow: starts with a written spec, writes a prompt from the spec, Codex generates 6 PRs for a single large feat…

Robert Erez argues AI is shifting CI/CD's primary design axis from 'how fast can we ship' to 'how do we manage risk.' Ephemeral environments are replacing static staging — each PR gets its own environ…

Data from Weave (10,000+ engineers). Top 1% AI-using engineering orgs: flatter organizational structure, higher agency ICs, deploy more frequently than average, and do NOT have more bugs despite highe…

Connecting AI to all your tools (Gmail, Calendar, Slack, Notion) solves the access problem but creates a new one — the AI can see everything but doesn't know what matters. The context layer is the sol…

Portkey describes their internal engineering practice: all shared context lives in .md files in a GitHub repo rather than Confluence/Notion, making it accessible to agents across the team. They built …

Addy Osmani describes 5 building blocks of agent loop engineering: automations, worktrees, skills, plugins/connectors, and sub-agents. Key warning: 'comprehension debt' — when engineers use agent loop…

Based on 20,000 enterprise agent deployments, Salesforce found that 90% of the work in AI agents is post-launch: monitoring, correction, iteration. Key practices: start small and narrow scope, define …

OpenAI built its internal data agent on a 'vanilla' architecture — the secret was stacking 6 layers of context across 90,000 tables to give the agent sufficient grounding. Used Codex to migrate 10,000…

The AI Maker piece frames the core failure modes of AI agents: "fake done" (agent claims completion without verifying) and "undefined done" (agent stops because it ran out of instructions, not because…