Making AI Agents Reliable Enough for Production

There's a running joke in AI: demos are easy, production is hard. Nowhere is this more true than with AI agents.

An agent that works 80% of the time in demos will frustrate users in production. We need reliability closer to 99%. Here's how I've been approaching this.

The Failure Modes

First, understand how agents fail:

1. Tool selection errors: Agent picks the wrong tool for the job
2. Parameter errors: Right tool, wrong arguments
3. Hallucinated actions: Agent "uses" a tool that doesn't exist
4. Infinite loops: Agent gets stuck retrying the same failing action
5. Context overflow: Agent loses track of earlier information
6. Scope creep: Agent goes off-task pursuing tangents

Strategies That Work

1. Constrain the Action Space

Don't give agents tools they don't need. Every tool is a potential failure point.

// Instead of
tools: [email, calendar, search, code, files, browser, ...]

// Use
tools: [search, read_file]  // Only what's needed

2. Validate Before Execution

Add a validation layer between the agent's decision and actual execution:

function executeAction(action: AgentAction) {
  const validation = validateAction(action);
  if (!validation.valid) {
    return feedbackToAgent(validation.error);
  }
  return execute(action);
}

3. Implement Retry with Backoff

Transient failures happen. Build in smart retries:

async function robustExecute(action, maxRetries = 3) {
  for (let i = 0; i < maxRetries; i++) {
    try {
      return await execute(action);
    } catch (error) {
      if (i === maxRetries - 1) throw error;
      await sleep(Math.pow(2, i) * 1000);
    }
  }
}

4. Set Clear Boundaries

Use system prompts that explicitly state what the agent should NOT do:

You are a code search assistant.
- DO search files and explain code
- DO NOT modify any files
- DO NOT execute code
- DO NOT make assumptions about missing information
- If unsure, ask the user for clarification

5. Add Circuit Breakers

Prevent runaway agents:

const MAX_ITERATIONS = 10;
const MAX_TOKENS = 50000;

let iterations = 0;
let tokenCount = 0;

while (!done && iterations < MAX_ITERATIONS && tokenCount < MAX_TOKENS) {
  // Agent loop
  iterations++;
  tokenCount += response.usage.total_tokens;
}

6. Human Checkpoints

For high-stakes actions, require confirmation:

if (action.type === 'delete' || action.type === 'send') {
  const approved = await requestHumanApproval(action);
  if (!approved) return { status: 'cancelled' };
}

Monitoring and Observability

You can't improve what you can't measure:

• Log every agent step with structured data
• Track success rates by task type
• Measure latency at each stage
• Alert on anomalies (too many retries, unusual patterns)

Testing Agents

Traditional unit tests aren't enough. You need:

• Scenario tests: Does the agent handle this specific workflow?
• Adversarial tests: What if the tool returns an error?
• Regression tests: Did this previously-working case break?
• Evaluation sets: A curated set of examples with expected outcomes

The Reality

Even with all these measures, agents won't be perfect. The goal is to:

1. Fail gracefully when failures happen
2. Make failures observable and debuggable
3. Continuously improve based on real usage

Production-ready agents are a journey, not a destination.