Building Agentic AI Systems That Actually Work
Most agentic AI demos look impressive but fall apart in production. Lessons from a year of designing reliable agent-based evaluation systems that handle real-world complexity.
There's a growing disconnect between the agentic AI systems that look impressive in demos and the ones that actually survive contact with production traffic. I've spent the past year building agent-based evaluation systems at a large tech company, and the lessons have been humbling.
The Reliability Problem
The core challenge with agentic systems isn't making them smart. It's making them predictably smart. When you chain multiple LLM calls together, each with its own failure mode, you're compounding uncertainty. A single agent call with 95% accuracy becomes a three-step chain with roughly 86% accuracy. That gap matters when you're processing thousands of evaluations per day.
We solved this by treating agent orchestration like distributed systems engineering. Each agent step gets its own retry policy, timeout budget, and fallback strategy. We implemented circuit breakers that detect when a particular model endpoint is degrading and reroute traffic to backup models. This isn't glamorous work, but it's the difference between a system that works in a notebook and one that works at scale.
Structured Outputs Are Non-Negotiable
Early on, we tried parsing free-form LLM responses. That lasted about two weeks before the edge cases buried us. Now every agent in our pipeline produces structured JSON output validated against a schema before it's accepted. If the output doesn't conform, we retry with a more constrained prompt. If it still fails, we route to a human reviewer.
// Every agent response goes through validation
const result = await agent.evaluate(input);
const parsed = AgentResponseSchema.safeParse(result);
if (!parsed.success) {
await retryWithConstrainedPrompt(input, parsed.error);
}This pattern alone eliminated roughly 40% of our production incidents. The lesson: treat LLM outputs with the same suspicion you'd treat user input.
Observability Changes Everything
Having worked on an observability platform previously, I brought an observability-first mindset to our agent systems. Every agent call is traced end-to-end with latency, token usage, model version, prompt template version, and output quality scores. When something goes wrong (and it will), you need to reconstruct exactly what the agent saw and decided.
We built a custom dashboard that shows agent decision trees in real-time. When an evaluation looks wrong, a reviewer can trace back through every reasoning step, see what context the agent had, and identify where it diverged from expected behavior. This feedback loop is what makes continuous improvement possible.
Design for Human-in-the-Loop from Day One
The systems that work best aren't fully autonomous. They're designed with clear escalation paths. Our agents have calibrated confidence scores, and anything below the threshold gets routed to human review. Over time, as we fine-tune prompts and add guardrails, that threshold tightens. But we never assume we can remove humans from the loop entirely. The goal is to amplify human judgment, not replace it.