🚀 Agents in Production
Learn Agents in Production on AI4AI — short, hands-on lessons with live AI runs, at three reading levels (beginner to expert). Free to start.
What you'll learn
- Designing Reliable Agents: Scope, Tools, and Success Criteria (8 min) — ⚡ A production agent fails most often not because the model is weak, but because the task was poorly defined before a s…
- Orchestration Patterns: Single vs. Multi-Agent (8 min) — ⚡ **Single-agent orchestration** runs one model in a loop: it receives a task, calls tools, observes results, and itera…
- State & Memory at Scale: Persisting, Summarizing, and Recovering (8 min) — Production agents are stateless by default: each call to a language model starts fresh. To maintain continuity across m…
- Guardrails & Approvals: Keeping Agents Safe in Production (8 min) — ⚡ Production AI agents can fail in expensive or irreversible ways: they may spin in infinite reasoning loops, rack up l…
- Observability: Tracing, Logging, and Debugging Production Agents (8 min) — Observability for agents means capturing enough structured data at runtime to answer 'what exactly happened, and why?' …
- Evaluating & Improving Live Agents (8 min) — ⚡ Once an agent is live, you need a feedback loop that catches problems before users do. This loop has three parts. Fir…
Lessons
Designing Reliable Agents: Scope, Tools, and Success Criteria
⚡ A production agent fails most often not because the model is weak, but because the task was poorly defined before a single token was generated. Reliable agent design starts with three decisions made before you write any code. First, scope the task tightly. Write one sentence d…
Orchestration Patterns: Single vs. Multi-Agent
⚡ **Single-agent orchestration** runs one model in a loop: it receives a task, calls tools, observes results, and iterates until done. This is the right default — lower latency, simpler debugging, and no inter-agent communication overhead. Use it whenever a task fits within one …
State & Memory at Scale: Persisting, Summarizing, and Recovering
Production agents are stateless by default: each call to a language model starts fresh. To maintain continuity across many steps or sessions, you must externalize state — storing conversation history, tool results, and task progress in a database or message queue outside the mod…
Guardrails & Approvals: Keeping Agents Safe in Production
⚡ Production AI agents can fail in expensive or irreversible ways: they may spin in infinite reasoning loops, rack up large API bills, execute destructive tool calls, or take actions in the real world that are hard to undo. Guardrails are the set of constraints and checkpoints y…
Observability: Tracing, Logging, and Debugging Production Agents
Observability for agents means capturing enough structured data at runtime to answer 'what exactly happened, and why?' without guessing. The three pillars are: **traces** (a tree of every LLM call and tool invocation for one user request, linked by a shared trace ID), **logs** (…
Evaluating & Improving Live Agents
⚡ Once an agent is live, you need a feedback loop that catches problems before users do. This loop has three parts. First, define success metrics tied to real outcomes: task completion rate (did the agent finish the job without human takeover?), answer correctness (verified by a…