AI Product Building AI Agents Architecture

Production agents route routine cases through decision trees, reserving humans for complexity

Handle exact matches and known patterns without AI; invoke the model for ambiguity, and route genuinely complex cases to human judgment

@vasuman — AI Agents 101 · Feb 24, 2026 · 16 connections

Most agent tutorials show every user input flowing through an LLM. Production agents do the opposite: structured decision logic handles the 80% of routine cases, the model handles genuinely ambiguous situations, and the complex 20% routes to human judgment. This addresses both cost and latency — the two things that kill agents in production.

The pattern is a direct extension of Declarative beats imperative when working with agents: the decision logic encodes known paths declaratively, while the LLM handles the ambiguous middle ground that can’t be pre-specified. It’s also why Verification is the single highest-leverage practice for agent-assisted coding matters more in production — when the LLM is only called for harder cases, each call is higher-stakes and needs verification. The orchestration layer, not the agent, enforces guardrails and permissions — validating requests, checking access, and feeding results back. This connects to why Context is the product, not the model applies at the system level, not just the prompt level.

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→ Context is the product, not the model → Declarative beats imperative when working with agents → Verification is the single highest-leverage practice for agent-assisted coding

Referenced by (13)

← Inference-time compute makes cost-per-outcome a choice — and that's the application layer's counterattack on the labs ← Humans should supervise agent loops from a leveraged point, not sit inside every one ← The intelligence lives in the workflow, not the model — and a model can't simply read it ← Policy enforcement must run independently of model cooperation — hooks, not prompt instructions ← SaaS survives as the governance and coordination layer — determinism still rules ← An orchestrator agent that manages other agents solves the parallel coordination problem without human bottleneck ← Enterprise agents need deterministic structure while startups need autonomous loops — same models, different harnesses ← Safety enforcement belongs in tool design, not system prompts ← AI is the computer — orchestration across 19 models is the product, not any single model ← The intelligence-to-judgement ratio determines which professions AI automates first ← Systems that prevent bad behavior beat moral appeals — design the cash register, not the sermon ← Detect everything, notify selectively — the observability-to-notification ratio determines system trust ← Intelligence location — code vs prompts — determines system fragility and flexibility