I've been building AI agents for a while now. Not chatbots. Not RAG demos. Real agents that take actions, make decisions, and run autonomously.
Here's what nobody tells you.
The gap between demo and production
Every AI agent framework shows you a 5-line demo that works perfectly. Then you deploy it, and it fails in ways you didn't imagine.
The reason is simple: a demo is a happy path. Production is a graph of failure states.
1. Observability is not optional
Your agent is only as good as your ability to see what it's doing.
When an agent makes a wrong decision, you need to know exactly why. Was it a bad prompt? A hallucinated tool call? Missing context from a previous step?
Log every thought. Audit every action. If you can't replay an agent's decision process, you can't trust it.
2. Tools are the bottleneck, not the LLM
Most people think the LLM is the hard part. It's not. The hard part is the tools.
Your agent needs to call APIs, read databases, write files, send emails. Each of those is a failure point. Network timeout. Auth expired. Schema changed. Rate limited.
Build your tool layer like you build a distributed system. Retries. Circuit breakers. Timeouts. Graceful degradation.
3. The context window is a trap
Long-running agents accumulate context. The more they do, the more context they carry. Eventually, the context window fills with noise, and the agent starts making bad decisions.
Strategies that actually work:
- Summarization — compress old context into summaries, don't carry raw history
- Structured memory — separate short-term (current task) from long-term (learned patterns)
- Forgetting — actively prune irrelevant context. If it didn't matter in the last 10 steps, it probably won't matter now
4. Determinism is underrated
Everyone wants creative agents. What you actually want is predictable agents.
A creative agent that hallucinates a solution is useless. A predictable agent that follows a known pattern is valuable.
Design for determinism first. Add creativity as a controlled parameter.
5. The orchestration layer matters more than the model
You can swap GPT-4 for Claude or Gemini and your agent still works — if your orchestration is solid.
Good orchestration:
- Clear state machine
- Explicit error handling
- Human-in-the-loop for critical decisions
- Audit trail for every action
The uncomfortable truth
Building AI agents that actually work is not an AI problem. It's a software engineering problem.
The LLM is the easiest part. Everything around it — tools, observability, state management, error handling, orchestration — that's where the real work is.
And that's also where the real value is.
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