Building AI Agents That Actually Work

The Agent Hype vs. Reality

Everyone's building AI agents right now. Most of them don't work reliably. After building several agent systems for real production use, here's what I've learned about making agents that actually deliver consistent results.

Key Principles

1. Constrain Before You Expand

The biggest mistake is giving an agent too much freedom too early. Start with the narrowest possible scope, get it working reliably, then gradually expand capabilities. An agent that does one thing perfectly is infinitely more useful than one that does ten things unreliably.

2. Tool Design Is Everything

The tools you give an agent define what it can do. Well-designed tools with clear descriptions, strict input validation, and predictable outputs will outperform any amount of prompt engineering.

# Good tool design: clear, constrained, predictable
def search_codebase(query: str, file_type: str = None, max_results: int = 10):
    """Search the codebase for files matching a query.

    Args:
        query: Search terms (supports regex)
        file_type: Filter by extension (e.g., '.py', '.js')
        max_results: Maximum results to return (1-50)

    Returns:
        List of {path, line_number, context} matches
    """

3. Structured Output Over Free-Form

Whenever possible, force structured outputs. JSON schemas, enums, and constrained responses reduce hallucination and make downstream processing reliable.

4. Fail Fast, Recover Gracefully

Agents will fail. The question is how. Build in explicit failure detection and recovery paths rather than hoping the LLM will figure it out.