Software Delivery Agent
Continuous Integration
On-demand Webinar
On-demand Webinar

Beyond Demos: Building Production-Ready AI Agents with Structured Enterprise Access | Harness Resource

As AI agents move past the “works on my machine” stage, the true challenge is equipping them with reliable, structured access to complex enterprise systems. Without it, AI agents are certain to be ineffective at enterprise scale.This community roundtable walks through how to evolve a data platform into a robust, agent-ready platform. We will focus on practical design patterns that make agents truly usable in real production environments.Key Takeaways:Discover the architecture behind this transformation, including the use of a semantic layer, knowledge graph principles, and MCP-based tool abstractions. Learn why structure, not just models, is the essential component for enabling reliable reasoning, robust governance, and seamless cross-system workflows.This session is designed for engineers and engineering leaders ready to move AI agent proofs-of-concept into secure, enterprise-scale production.

Published
June 16, 2026

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What you'll learn

Key Takeaways

Provide a semantic layer for runtime understanding

Agents require a structured semantic layer to comprehend data relationships dynamically. This prevents context window bloat by providing only necessary information instead of raw data dumps.

Use knowledge graphs for multi-hop reliability

Mapping explicit relationships between entities like commits, pipelines, and deployments removes guesswork. This structure allows agents to reliably traverse complex data without hallucinating.

Embed policies directly into the semantic model

Integrating policy evaluation into the data model ensures agents understand execution guardrails before taking action. This prevents autonomous agents from causing system incidents or violating freeze windows.

Expose structured data via MCP servers

Delivering the semantic layer through the Model Context Protocol allows developers to interact with the platform using their preferred coding agents. The focus should be on making data progressively discoverable and composable.

Implement rigorous evaluations for agent actions

Just as code requires unit tests, non-deterministic agents need evaluations to measure cost, efficiency, and correctness. Start with small, common use cases and iterate based on these performance metrics.