Building Reliable Agents

As we move from simple chatbots to autonomous agents, reliability becomes the primary bottleneck. It’s one thing to get a good response from an LLM; it’s another to build a system that can reliably execute a multi-step task without getting lost or hallucinating.

The Loop

The core of any agent is the Think-Act-Observe loop.

1. Think: Analyze the current state and decide on the next action.
2. Act: Execute the tool or API call.
3. Observe: Read the output of the action.

Here is a simple Python example of how we might structure this loop:

def run_agent_loop(goal, max_steps=10):
    memory = [f"Goal: {goal}"]
    
    for _ in range(max_steps):
        # 1. Think
        next_action = llm.predict(memory)
        
        if next_action == "DONE":
            return "Task completed."
            
        # 2. Act
        tool, args = parse_action(next_action)
        result = execute_tool(tool, args)
        
        # 3. Observe
        memory.append(f"Action: {next_action}")
        memory.append(f"Observation: {result}")
        
    return "Max steps reached."

State Management

One of the biggest challenges is managing the context window. As the conversation grows, we need strategies to:

• Summarize past actions.
• Prune irrelevant details.
• Keep the “Goal” visible at all times.

Conclusion

Reliability isn’t just about better models; it’s about better scaffolding around those models. By treating LLMs as components in a larger state machine rather than magic boxes, we can build agents that actually work in production.