Turing Post recommends: Memory, state, security, evals, and the data layer determine what actually works.
Agent prototypes are easy. Surviving security review, cost ceilings, and production traffic is the hard part. In our recent blog post, "Designing an Agentic Platform: The Infrastructure That Makes Agents Work," we make the case that the issue usually isn't a weak model; it's thin infrastructure around the model.
In the post, you'll see how to:
Separate the agent, the harness, and the platform so you aren't rebuilding the same scaffolding one project at a time
Design memory, state, orchestration, evals, security, and observability as first-class parts of the system instead of afterthoughts
Treat durable data, governance, and cost controls as shared platform services rather than ad hoc fixes
If your team is moving beyond prototypes and making architecture decisions now, this framework offers a clearer way to understand why agent projects stall and where MongoDB Atlas can serve as the unified data and infrastructure layer your agents rely on.
👨🔧 This case is presented by the MongoDB team. We thank MongoDB for sharing their expertise and supporting Turing Post’s mission to bring clarity to the AI landscape.
