How to Build a Self-Correcting AI Agent Microservice

Build a self-correcting AI agent microservice online on 15 August 2026, learning how runtime validation can make LLM-powered backends more reliable. Hosted by Onyeka O. for PyData Huddersfield, this hands-on session focuses on a central challenge in production AI engineering: large language models are non-deterministic, while backend systems and APIs require predictable, correctly structured data. Rather than simply calling an LLM and trusting its response, you’ll learn how to place strict software guardrails around an agent so failures can be detected and corrected before they reach production services. ## What you’ll build The workshop centres on an AI agent microservice engineered for backend reliability. The implementation will use runtime schema validation to inspect the agent’s output, trap errors and prevent invalid results from touching production APIs. Those failures are then fed back into the agent, allowing it to recognise the problem and autonomously correct its response. The session will cover the practical relationship between: - Non-deterministic LLM outputs and deterministic software requirements - Runtime schema validation as a reliability boundary - Error trapping before downstream API calls - Feeding validation failures back into an AI agent - Autonomous recovery from malformed or incorrect outputs - Designing AI microservices with production safety in mind This makes the event especially relevant to software engineers, backend developers, AI/ML practitioners and builders exploring agentic systems. It should also appeal to anyone investigating how AI agents can move beyond demos and become dependable components within larger software architectures. No specific experience level or prerequisite technology is stated, so attendees should use the workshop description to assess whether the engineering focus matches their background. ## Format and event details This is an online, hands-on workshop taking place at 11:00 AM on Saturday, 15 August 2026. The access link will be visible to registered attendees. The event is presented by PyData Huddersfield and sponsored by NumFOCUS, an organisation that promotes open code for better science. The standout focus is not merely generating an LLM response, but building a feedback loop that validates, rejects and repairs bad outputs before they can affect production APIs—a practical pattern for more robust AI auto