Building AI Agents That Actually Work

After building dozens of LLM-powered applications, I've noticed a pattern. Everyone starts with the same naive approach: chain together some prompts, add a few tools, and hope for the best. Then reality hits.

The agent loops forever. It hallucinates tool calls. It forgets what it was doing three steps ago. And debugging it feels like watching a toddler play chess — sometimes brilliant, often nonsensical.

Why Most Agents Fail

Most failures come from one fundamental misunderstanding: LLMs are stateless. Every inference is a fresh start. Yet we build agents that implicitly assume they'll "remember" the conversation history without explicitly managing it.

Here's the key insight: treat your agent like a distributed system. It has:

- A control plane (the orchestration logic)
- Data planes (memory, tools, external APIs)
- Clear boundaries between them

The Memory Problem

I used to pass the entire conversation history to every model call. It worked until context windows filled up, costs ballooned, and older information got buried under newer tokens.

Now I use a tiered approach:

1. Working memory (last 10 turns)
2. Summarized memory (key facts, decisions)
3. Vector store (semantic retrieval)
4. Structured database (persistent state)

The agent decides what to remember and where. This isn't just engineering overhead — it makes the system more predictable.

Tool Design Matters

Your agent is only as good as its tools. I follow two rules:

Be specific. Instead of "search_files", use "find_recent_errors_in_logs". The more constrained the tool, the less room for hallucination.

Fail fast. Tools should validate inputs and return clear errors, not silently guess. The agent can handle explicit failures better than subtle bugs.

The Debugging Imperative

The hardest part of agent development isn't making it work — it's understanding why it failed. Every agent framework I build now includes:

- Complete traces of every decision
- Token usage per step
- Tool call latency
- State transitions visualized

Without this, you're flying blind. With it, you can iterate fast.

Where We're Heading

The next generation of agents won't be monolithic. They'll be compositions of smaller, verifiable units — microservices for reasoning. Each unit has clear inputs, outputs, and failure modes.

We're building this at Sunset Beach. Not because it's elegant, but because it's the only way to ship reliable AI at scale.