HOW TO BUILD YOUR OWN AI AGENT: THE COMPLETE ARCHITECTURE FOR ENGINEERING TEAMS
summary
Architectural blueprint for a production agent that ties system context, tool descriptions, and just-in-time knowledge via an ingestion pipeline from git Notion Slack Jira into embeddings stored in a vector database, plus memory and multi-agent orchestration over MCP and REST. Execution uses an agentic loop where the LLM requests tools and the agent enforces guardrails or human approvals. Observability relies on OpenTelemetry tracing all inputs, outputs and tool calls. Cost control uses a cheap classifier to route requests and avoid bills reaching hundreds of thousands or millions per month.
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chapters (11)
- 0:00Why build custom agents
- 3:20Core agent architecture
- 5:03System context design
- 6:26Tool integration strategies
- 8:55Semantic search for knowledge
- 12:48Orchestrating multiple agents
- 14:56Agent communication protocols
- 15:59Security and guardrails
- 17:44Observability challenges
- 20:12Cost control for AI agents
- 21:55Closing and next steps
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