Guides
Build your agent stack
Opinionated, index-backed decision guides — the real trade-offs for choosing each layer of an AI agent stack, walked the way a senior engineer would. No rankings for hire, no vendor’s roadmap.
RAG
How to build a RAG knowledge assistant
Platform or compose-it-yourself, then the seven layers — ingestion, chunking, vector store, retrieval, orchestration, memory, and eval.
Voice
How to build a voice AI agent
Latency is the tyrant. Platform vs compose, then telephony, speech-to-text, the LLM, text-to-speech, turn-taking, and testing.
Coding
How to build a coding agent
Adopt an agent or build your own harness, then the layers — the agent loop, code context, safe execution, the model, memory, and review.
Browser
How to build a browser-automation agent
Perception is the hard part, not clicking. DOM vs vision vs VLA, then the runtime, the driver, the model, planning, and reliability.
Autonomous
How to build an autonomous agent
Autonomy is a control problem, not a capability problem. Adopt, build, or hand-roll — then framework, tools, memory, execution, orchestration, and governance.
MCP
How to build an MCP server
The protocol is trivial; tool design is the craft. Wrap, build, or aggregate — then SDK and transport, tool design, resources, reliability, and distribution.
Self-hosted
The self-hosted agent stack
Run agents on your own infrastructure. What you're actually protecting, the platform, the model question, safe execution, data, and boring ops.
Frameworks
How to choose an agent framework
Do you need one at all? The five archetypes — code-first, role-based, typed, TypeScript-native, visual — and the criteria that actually predict regret.
Workflow
How to build AI workflow automation
Most “agents” should be workflows with LLM steps. Triggers, the engine, the LLM step, approval gates, reliability — and when to graduate to an agent loop.