Agent Reliability

The Problem with Agents

They look great on YouTube, but autonomous agents in production are chaotic. They hallucinate parameters, get stuck in tool-use loops, drop context midway, or confidently take destructive actions.

Symptoms You'll Recognise

• Your agent frequently calls the right tool with completely wrong JSON schemas.
• Infinite looping where the agent gets an error, tries the exact same thing again, and burns tokens.
• Agents "forgetting" the primary user goal after completing 3 or 4 sub-tasks.
• Total unreliability—it works flawlessly once, but fails the next 5 times on the same input.

Why It Happens

Building agents as single "while loops" with a huge system prompt is fundamentally flawed. When context windows get stuffed with tool descriptions, intermediate thoughts, and raw API responses, models lose their reasoning capability and drift from the goal.

How We Fix It

1. State Machine Architecture: We migrate your agents from free-form ReAct loops to deterministic state graphs (like LangGraph or AWS Step Functions).
2. Tool Hardening: We rewrite your tool definitions and implement strict Pydantic/Zod validation layers before the LLM can execute anything.
3. Memory Management: Implementing conversational scoping, summarizing old context intelligently so the agent stays focused on the immediate task.
4. Guardrails & Supervisor Approval: Adding logic that forces the agent to ask permission before executing destructive actions, or validates output against rules before replying to the user.

Proof

Stabilized an AI customer support agent handling order modifications. Reduced error rates from 34% (stuck in loops) to <2%, saving thousands in API costs and human escalations.