Building a Context Layer for Production AI Agents
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the gist
Production agents fail because they lack business-specific context. To succeed, companies must treat context as versioned, testable infrastructure—a 'GitHub for context'—that manages skills, dependencies, and compounding learning loops.
The Shift to Context-Driven Agents
Most AI agents fail in production because they lack the situated knowledge—business definitions, operational norms, and procedural playbooks—that human experts use to perform effectively. While model intelligence has increased exponentially, the ability for agents to access and apply company-specific context has remained stagnant. Performance in real-world business settings is a function of both intelligence and context, and the latter is currently trapped in siloed dashboards, Slack threads, and individual employee knowledge.
Architecture of a Context Layer
To move beyond isolated, brittle agents, organizations must implement a centralized context layer that functions as a 'company brain.' This layer acts as a repository for domain-specific skills, semantic definitions, and data graphs. Key requirements for this infrastructure include:
- Lifecycle Management: Treat context as code. Skills require versioning, dependency management, and clear ownership to prevent downstream breakage when positioning or business logic changes.
- Compounding Learning Loops: Implement harnesses that analyze agent traces to reverse-construct knowledge. This allows the system to suggest improvements to maintainers, creating a self-improving feedback loop.
- Openness and Portability: Ensure the context layer is framework-agnostic. It should support multiple retrieval mechanisms like SQL, vector search, and protocols like MCP to remain compatible with various agentic frameworks.
- Reverse Construction: Mine existing business systems (Salesforce, HubSpot, data warehouses) to automatically map connections between entities and metrics, which serves as the initial seed for the company brain.
Managing Context at Scale
As teams deploy dozens of agents, they face 'context sprawl' and security risks like hardcoded secrets in environment files. A robust context layer solves this by providing a unified workspace where human experts and AI agents collaborate. By encoding culture, norms, and business logic into this layer, companies differentiate their AI systems from competitors using the same base models. Context is not just a technical requirement; it is the core intellectual property of an autonomous firm.