Building an Internal Agent Platform
Provide shared infrastructure so teams can build agents safely: model access, approved tools, logging, evals, and guardrails as a service.
The first team in your organization to build an agent solves a dozen problems along the way: getting an API key, deciding which model to use, connecting to internal systems, logging what the agent does, keeping customer data out of traces, testing changes, approving risky actions. The second team solves the same dozen problems again, slightly differently. By the fifth team, you have five sets of API keys in five places, five logging formats, no shared view of cost, and no consistent way to answer "what did our agents do yesterday?"
An internal agent platform is the shared infrastructure that solves these problems once, so teams can build agents quickly and safely. This lesson covers when you need one, the core components (model access, approved tools, identity, logging, evals, and guardrails), how to design it as a paved road rather than a gate, and what to build first.
When you need a platform, and when you don't
A platform is overhead. Building one before you need it slows everyone down and produces infrastructure designed for imagined requirements. Signals that it's time:
- Several teams are building or running agents, not just one.
- The same questions keep recurring: "how do we get access to the model?", "where do the logs go?", "is this MCP server approved?"
- Nobody can answer basic questions about total AI spend, which agents exist, or what they can access.
- Security or compliance is asking for audit trails and data controls that individual teams can't each provide.
- Good work isn't spreading. One team built a solid eval setup or a useful internal tool and nobody else can reuse it.
If you have one team and two agents, you don't need a platform. You need good habits and a shared document. Start there, and extract a platform from what teams have already built once the patterns are clear.