They’re built like chatbots, not systems. The internet tells you: “Add tools.” “Add memory.” “Add RAG.” That’s not why agents break in production. They break because nobody defines control. Here’s the real mental model I use when building AI agents 👇 1. An agent is a decision system, not a response generator LLMs are good at choosing actions. They are terrible at being left unsupervised. If your agent can act, you must define: when it can act how often with what confidence and what happens when it’s wrong 2. Tools are liabilities, not features Every tool you add: increases failure modes increases blast radius increases cost Production agents don’t have “many tools”. They have the minimum set required to complete one job. 3. Memory is where agents quietly go off the rails Most teams store everything. Good agents store decisions, not conversations. Memory should answer: “What must this agent remember to make a better next decision?” Nothing more. 4. Planning is optional. Verification is not. Planning looks impressive in demos. Verification saves you in production. Every agent should ask: “Did this tool call succeed?” “Does this output meet the contract?” “Do I need to retry or stop?” If your agent can’t say no, it’s not autonomous — it’s reckless. 5. Evaluation is the real intelligence layer Without evals: you don’t know if it’s improving you don’t know if it’s degrading you don’t know if it’s safe No evals = no agent. Just vibes. The hard truth: Most “agent frameworks” optimize for demos. Real agents optimize for control, observability, and failure handling.
That’s what separates: - toy agents - systems that companies trust The graphic attached isn’t a checklist. It’s the minimum architecture for an agent you can sleep on.
They’re built like chatbots, not systems.
The internet tells you:
“Add tools.”
“Add memory.”
“Add RAG.”
That’s not why agents break in production.
They break because nobody defines control.
Here’s the real mental model I use when building AI agents 👇
1. An agent is a decision system, not a response generator
LLMs are good at choosing actions.
They are terrible at being left unsupervised.
If your agent can act, you must define:
when it can act
how often
with what confidence
and what happens when it’s wrong
2. Tools are liabilities, not features
Every tool you add:
increases failure modes
increases blast radius
increases cost
Production agents don’t have “many tools”.
They have the minimum set required to complete one job.
3. Memory is where agents quietly go off the rails
Most teams store everything.
Good agents store decisions, not conversations.
Memory should answer:
“What must this agent remember to make a better next decision?”
Nothing more.
4. Planning is optional. Verification is not.
Planning looks impressive in demos.
Verification saves you in production.
Every agent should ask:
“Did this tool call succeed?”
“Does this output meet the contract?”
“Do I need to retry or stop?”
If your agent can’t say no, it’s not autonomous — it’s reckless.
5. Evaluation is the real intelligence layer
Without evals:
you don’t know if it’s improving
you don’t know if it’s degrading
you don’t know if it’s safe
No evals = no agent. Just vibes.
The hard truth:
Most “agent frameworks” optimize for demos.
Real agents optimize for control, observability, and failure handling.