agents

Make loops trustworthy. Adversarial verification panels, sub-agent orchestration, loop-until-dry for unbounded discovery, loop-until-budget for paid depth, multi-modal sweeps with diverse prompts, eval-driven cap selection, and the five anti-patterns — silent caps, infinite plans, drift, premature termination, thrashing — that ship to prod more than they should.

Once an agent loop runs past a few iterations the failure surface shifts: state bloats, tool calls fail in three distinct ways, retries multiply if you stack them wrong, and unbounded budgets rack up four-figure bills overnight. The state, errors, and budgets you need to make the loop survive contact with reality.

How LLM agents actually run: the iterative prompt-action-observation loop, the ReAct shape, the smallest tool-calling loop in twelve lines, why you always stack three termination layers, what the model sees on iteration N, and when a single prompt is the better answer.

A rigorous look at how to measure agent performance — trajectory-level vs final-answer evals, canonical multi-step benchmarks (SWE-bench, WebArena, OSWorld, GAIA), LLM-as-judge pitfalls, and why your eval is your spec.

How to manage, compress, and strategically fill the context window in long-horizon agents — covering summarization checkpoints, scratchpad memory, retrieval injection, prompt caching, and compaction triggers.

Move beyond toy ReAct loops. Learn how to build a production-grade agent harness with a robust control loop, tool registry, schema validation, retry logic, token budgets, abort signals, and a persistent journal that survives crashes.

Go beyond naive RAG pipelines. Learn how Self-RAG, Corrective RAG, and retrieval-as-tool patterns let an LLM decide when, what, and how many times to retrieve — enabling reliable multi-hop reasoning over complex knowledge bases.

A tour of the underused Claude Code power features beyond basic prompting: CLAUDE.md memory, plan mode, rewind, custom slash commands, subagents, hooks, MCP servers, statusline, and headless mode.

What LangGraph adds over plain LangChain, how its nodes-and-edges model maps to real agent patterns (loops, branches, human approvals), and when to reach for it versus a simpler control flow.

What separates an agent from a plain chatbot, the perceive-think-act loop they all share, and how to design one that doesn't loop forever or burn through your token budget.