Stop Building AI Agents Like Scripts: A Loop Architecture Primer for Devs

The Problem With Most AI Agent Code

If you've shipped an AI agent recently, there's a decent chance it's basically a fancy script. You send a prompt, get a response, maybe chain a few calls together, log the output, done. That works fine for demos. But the moment you need that agent to:

• Decide whether its own output is good enough to proceed
• Spawn sub-agents to handle specific tasks
• Recover from API errors or hallucinated nonsense
• Explain why it made a decision three steps ago

...your script falls apart.

What you're missing is loop architecture — the control structure that governs how agents reason, act, evaluate, and decide what to do next. Most teams treat this as an afterthought. It shouldn't be.

What Loop Architecture Actually Looks Like

Think of a loop architecture as the runtime for your agent. It's not the model, and it's not the prompt. It's the scaffolding that wraps around both and enforces:

• State management: What has the agent done? What does it know?
• Evaluation logic: Did that action succeed? Should we retry, delegate, or stop?
• Control flow: What happens next? Do we loop again, call a different agent, or return to the user?

Here's a toy example in pseudocode:

class AgentLoop:
    def __init__(self, task, max_iterations=5):
        self.task = task
        self.state = {"steps": [], "status": "running"}
        self.max_iterations = max_iterations

    def run(self):
        iteration = 0
        while self.state["status"] == "running" and iteration < self.max_iterations:
            action = self.reason(self.state)
            result = self.execute(action)
            self.state = self.evaluate(result, self.state)
            iteration += 1
        return self.state

    def reason(self, state):
        # LLM call: "given state, what should I do next?"
        pass

    def execute(self, action):
        # Actually do the thing (API call, DB query, spawn sub-agent)
        pass

    def evaluate(self, result, state):
        # LLM or deterministic check: did it work? update state accordingly
        pass

Notice the loop isn't endless. It has a budget. It checks its own output. It maintains state across iterations. That's the foundation.

The Failure Modes You'll Hit

Once you start building loops, you'll encounter these failure modes fast:

Runaway Spawning

Your orchestrator agent decides every subtask needs its own agent. Suddenly you've got 47 LLM calls in parallel, your API quota is toast, and you have no idea which one caused the failure.

Fix: Hard limits on spawn depth and breadth. Track the agent tree explicitly.

State Amnesia

The agent forgets what it did two steps ago because you're not persisting state between calls. It repeats work, contradicts itself, or loops forever.

Fix: Structured state (JSON, not vibes). Log every state transition. Make it queryable.

No Exit Strategy

Your loop has no clear success or failure condition. It just… keeps going until it times out.

Fix: Explicit halt conditions. "Task complete", "unrecoverable error", "max budget exceeded". Treat these as first-class citizens in your control flow.

Governance Isn't Optional (Especially in the UK)

If you're building agents for finance, healthcare, legal, or public sector, you can't punt on governance. Loop architecture is where you enforce:

• Audit trails: every decision, every action, every state change
• Human-in-the-loop gates: certain actions require approval before execution
• Rollback and replay: if something breaks, you can rewind and debug

This isn't theoretical. If your agent makes a decision that costs money or affects people, you need to be able to explain why it did that. That explanation lives in your loop architecture, not in a vibe check of your prompt history.

For a deeper dive into how loop architecture intersects with governance and enterprise constraints, the original piece on engineering discipline is worth reading.

Practical Next Steps

If you're refactoring an existing agent or starting fresh:

1. Make state explicit. Use a schema. Version it. Persist it.
2. Define halt conditions upfront. Success, failure, budget exhaustion.
3. Instrument everything. Log state transitions, decisions, and evaluations. You'll need this when things break.
4. Set hard limits. Max iterations, max spawn depth, max cost per loop.
5. Build eval into the loop. Don't wait until prod to discover your agent hallucinates half the time.

If you're working with teams who need to scale this across regulated environments, agencies focused on AI automation and software development often have reusable loop patterns and governance templates baked in.

The Takeaway

Loop architecture is the difference between a prototype that impresses in a demo and a system you can actually trust in production. Most teams skip this step because it's not as exciting as fine-tuning models or crafting clever prompts. But it's the scaffolding that makes everything else work.

Treat your agent runtime like you'd treat any other critical system component: with discipline, observability, and respect for failure modes. Your future on-call self will thank you.