Beyond the Prompt: Building Resilient AI Agents with Harness Engineering

Building autonomous AI agents often comes with real-world challenges such as hallucinations, context overload, and fragile tool integrations . While Large Language Models provide the core intelligence, a significant portion of an agent’s reliability depends on the engineering systems built around it .

In this session, we will explore Harness Engineering—the software layer that transforms probabilistic LLMs into deterministic, reliable, and self-correcting systems . You’ll learn how techniques like state management, sandboxed tool execution, and structured observation loops improve agent performance and robustness.

The session will include a live demonstration of an AI agent that writes, fails, and autonomously recovers its own pipeline , showcasing how resilient systems are built in practice.

This is a deep-dive, practical session designed to help practitioners build production-ready, reliable AI agents.

Key Takeaways:

• Challenges in AI Agents – understanding failure modes like hallucinations and tool errors
• Harness Engineering – building reliable systems around LLMs
• State & Context Management – controlling memory and execution flow
• Tool Integration – safe and sandboxed execution of external tools
• Self-Correcting Systems – designing observation loops for agent reliability
• Practical Demo – real-time agent debugging and recovery

Lavanya Pedada is an AI Architect at enGen with expertise in Machine Learning, Computer Vision, and Generative AI.

She has previously worked as a Principal Engineer at Verizon and held data science roles at Microsoft and EPAM Systems, focusing on building AI-driven solutions for complex business problems.