Everyone talks about AI agents.
Few people actually build one that works.
After designing multiple agent systems, here’s the simplest architecture that actually holds in production.
Step 1: Define a single-purpose agent
Your first agent should NOT be general-purpose.
Bad:
- “AI assistant”
Good:
- “Summarize customer feedback into actionable insights”
The narrower the scope, the higher the reliability.
Step 2: Implement the agent loop
Every functional agent relies on a decision loop:
while not done: observe_state() plan_next_action() execute() evaluate()
This loop is the core of autonomy.
Step 3: Tool-first design
Agents become useful when they can act.
Typical tools:
- APIs
- Databases
- Internal functions
Best practices:
- Validate inputs
- Restrict permissions
- Add retry logic
- Log everything
Step 4: Memory (don’t overdo it)
You need two layers:
- Short-term memory → current task
- Long-term memory → optional (vector DB)
Most early systems only need short-term context.
Step 5: Define exit conditions
Agents fail when they don’t know when to stop.
Always define:
- success criteria
- max iterations
- fallback (human escalation)
Step 6: Observability
If you can’t debug it, you can’t scale it.
Track:
- decisions
- tool calls
- failures
- reasoning steps
Common mistakes
- Overbuilding (multi-agent too early)
- No constraints
- No logging
- Vague objectives
Final insight
The LLM is not the hard part.
The system design is.
If you’re building your first agent:
Keep it simple.
Make it deterministic.
Then scale.
Full article:
https://brainpath.io/blog/how-to-design-first-ai-agent-system
Top comments (0)