AI AGENTS

A Show HN PDF form demo points at a bigger architecture shift: keep sensitive documents local, expose narrow browser tools to the model, and make AI assistance inspectable.

A deep comparison of Codex's new /goal loop and Claude managed agents outcomes, with practical workflow examples, control tradeoffs, and migration guidance for long-running tasks.

A long-form technical read on Flue from Fred K Schott, with deeper comparisons against OpenAI Agents, Vercel AI SDK, Google ADK, LangChain, Deep Agents, and CrewAI, plus practical production patterns.

A long-running coding agent is only useful if the environment around it can queue tasks, capture logs, checkpoint state, verify behavior, limit cost, and recover from failure.

Most agent tool APIs are just REST endpoints with nicer names. Production agents need intent-shaped tools that compress workflows, reduce context, and return reviewable receipts.

Skills turn a general coding agent into a trained teammate by packaging runbooks, scripts, examples, and domain-specific judgment into reusable instructions.

Warp going open source is not just a terminal story. It is a signal that AI coding tools are shifting from chat UX toward agent operations, where planning, execution, review, and feedback loops live close to the shell.

I told an agent to improve the site every 10 minutes and went to sleep. Here is what 12 new repos, 60 PRs, and three goofs taught me about overnight orchestration.

A practical architecture for multi-step Claude agents. Loop patterns, state management, error recovery, and the production gotchas that turn a five-step demo into a 20 percent success rate at scale.

Build MCP servers that connect Claude to your databases, APIs, and tools. Architecture, TypeScript SDK code, debugging, and the production gaps the spec doesn't cover.

Master tool use in the Claude API. Schema design, retry logic, multi-step loops, and the failure modes that only show up at 10k calls a day.

Five worked examples showing how the new Developers Digest products plug into each other. Real agent filesystems, auto-snapshots, gated skill libraries, eval suites, and a recursive MCP host.

agentfs is filesystem-shaped storage for AI agents. Postgres-backed on Neon, no cold starts, no exec by design. Pay-only plans start at twenty dollars.

Ten private tools shipped overnight - observability, skills, hooks, prompts, and evals - aimed at the agent infrastructure gap small teams keep falling into.

The math of agent pipelines is brutal. 85% reliability per step compounds to about 20% at 10 steps. Here is why long chains collapse in production, and the six patterns the field has converged on to fight the decay.

From single-agent baselines to multi-level hierarchies, these are the seven patterns for wiring AI agents together in production. Each with a decision rule, an implementation sketch, and the tradeoffs that actually matter.

Five managed-agent providers, five pricing models, zero unified cost attribution. If you're running agents overnight, you need FinOps you don't have yet.

Four agents, same tasks. Honest trade-offs from a developer shipping production apps with all of them.

CLAUDE.md is the highest-leverage file in any Claude Code project. Here's what goes in one, what doesn't, and the patterns that actually ship.

Autocomplete wrote the line. Agents write the pull request. The shift from Copilot to Claude Code, Cursor Agent, and Devin - explained with links to the docs that prove every claim.