9 docs tagged with "agents"

Portable, on-demand workflow packages for coding agents - what they are, how they differ from rules and project memory, how to use them across tools, and how to author your own with examples.

The AI coding-agent harnesses I run in the terminal -- opencode, pi, and herdr -- what a harness is, how they differ, and how I use them day to day.

What makes an LLM system an agent, how tool use works, the canonical multi-agent patterns, and the MCP and A2A protocols that connect agents to tools and to each other.

Using LLM coding agents inside an engineering workflow - the alignment-before-generation methodologies (SPDD, architect-as-orchestrator), architecture patterns that suit AI (deep modules, vertical slices), and the economics driving adoption.

The context window as a finite budget, why prompt engineering grew into context engineering, context rot, and the long-horizon techniques - compaction, structured note-taking, sub-agents, and just-in-time retrieval.

Approval gates, escalation, and accountability patterns for agents and LLM features that act in the real world - maker-checker, confidence thresholds, and audit trails.

Always-on context for coding agents - AGENTS.md, CLAUDE.md, Cursor rules, and how to split conventions from on-demand skills.

Getting reliable JSON and schema-bound responses from LLMs - native structured output modes, validation and repair loops, and when structure beats free-form prose.

A map of the AI application tooling landscape - -orchestration frameworks, connectivity protocols, vector databases, evaluation and observability, and the LLMOps discipline that ties them together.