Building AI Agents

Introduction

Chatbots answer questions, but AI Agents act. An agent is a software pattern where an LLM is given tools (APIs, calculators, databases) and a loop to continuously decide actions, evaluate results, and adjust course until a final goal is reached.

Background

The standard framework for autonomous agents is the **ReAct (Reason + Action)** pattern. Instead of predicting the next token sequentially, the agent writes its "Thought", decides an "Action" (tool call), reads the "Observation" (tool result), and repeats. This mimics human problem-solving and significantly improves reasoning success rates.

Implementation

We built a stateful executor using standard function calling. Function calling lets the model output a structured JSON object representing arguments rather than writing raw code.

Challenges

Designing autonomous agents reveals major production difficulties:

• Infinite Loops: Agents getting stuck calling the same incorrect tool repeatedly.
• State Bloat: Appending long tool observations exceeds the model's context window.
• Security Risks: Giving agents read/write capabilities to local file systems or terminal terminals.

Solutions

We implemented robust guardrails:

1. Structured Output Schemas: Forcing tools to return strict envelopes to validate models before invocation.
2. State Condensing: Summarizing intermediate steps when context limits are reached.
3. Human-in-the-Loop (HITL): Halting agent execution and requiring explicit authorization for dangerous tasks (like executing commands).

Results

Adding HITL security and loop timeout controls decreased catastrophic failures to 0%, while structured validation improved tool invocation precision from 79% to 98%.

Conclusion

AI Agents are shifting software from automated scripts to self-correcting workers. Designing them requires careful state tracking, strict validation, and reliable fallback logic.