AI Agents & LLM Systems
Building production agentic pipelines with tool use, orchestration, and guardrails.
What makes an agent production-grade
A demo agent calls a model in a loop until it produces a plausible answer. A production agent does the same thing inside hard boundaries: a fixed tool surface, a step budget, explicit error handling for every tool call, and a way to fall back to a human when confidence is low.
The architectural shift that matters most is treating the model as a planner over a small, well-typed set of tools rather than as a general-purpose executor. Every tool the agent can call should validate its own inputs and return structured, bounded output — the agent should never be trusted to self-limit.
Orchestration patterns we use
For most client work we reach for one of three patterns depending on task shape: a single-agent loop with tool use for well-scoped tasks, a router-plus-specialist pattern where a lightweight classifier hands off to task-specific prompts, and a supervisor/worker pattern for multi-step workflows that need intermediate verification.
- Single-agent + tools — support triage, structured data extraction
- Router + specialists — multi-domain assistants (billing vs. technical vs. sales)
- Supervisor/worker — long-running workflows (report generation, multi-source research)
Guardrails that actually hold
Schema-validate every model output before it touches a database, an email send, or a payment. Cap tool-call recursion depth. Log the full transcript (prompt, tool calls, outputs) for every production run — not just the final answer — so failures are debuggable after the fact rather than only reproducible by luck.
Working through something like this?
Get a scored blueprint in the Architecture Lab, or talk it through with the Architect Assistant first.