Session overview
This 48-minute session covers the architecture patterns we use to deploy AI agents into production environments — beyond the demos and into the systems that real users depend on.
What we cover
- The bounded autonomy principle. Why production agents need narrow domains, not unbounded freedom.
- Four-part architecture. Planner, tools, memory, and critic — what each part does and how they compose.
- Tool design. Why narrow, typed, idempotent tools beat "do anything" interfaces.
- Memory patterns. Conversation memory, user memory, knowledge memory — kept separate.
- Escalation logic. The most underrated capability — knowing when not to answer.
- Guardrails. Input, output, and action validation.
- Observability. Tracing every step of the agent's reasoning for production debugging.
Live demonstration
The session includes a live walkthrough of an agent we built for a customer support automation use case, including:
- The RAG layer connecting to the client's knowledge base.
- Tool integrations with the CRM and account system.
- The escalation classifier that routes uncertain conversations to humans.
- Production observability and how we debug live issues.
Q&A topics
The session ended with audience Q&A covering:
- How to evaluate agent quality during development.
- Cost management for agentic systems.
- When to use frontier models vs smaller ones.
- Self-hosted vs cloud trade-offs for agent deployments.
Who this is for
Engineering and product leaders considering AI agent deployments for their products. Useful background reading: our production AI agents guide and RAG vs fine-tuning decision tree.
Recording
Contact us to request the recording or a private replay session for your team.