Hallucinations in AI agents are not a bug to be patched at the application layer, they are a symptom of missing infrastructure. That is the central insight from Aditya Mulik's experience building an inventory recommendation system, and it is one every team deploying LLMs into production needs to internalize. When his system began confidently recommending products that did not exist, the fix was not a better prompt or a different model. It was treating the entire LLM stack as a shared platform concern, complete with prompt registries, versioning, schema enforcement, and per-request token cost attribution.
This reframing matters because most teams still build AI features the way they build traditional software: each application owns its own integration, its own prompt logic, and its own error handling. That approach works until it does not. At scale, unversioned prompts drift silently. Schema mismatches between what the model outputs and what the system expects compound into nonsense. And without token-level cost attribution, teams have no visibility into which requests are burning budget on hallucinated outputs. Mulik's solution, a centralized LLM platform with these common services, mirrors the evolution of infrastructure itself. We moved from each server managing its own networking to shared load balancers and service meshes. AI agents deserve the same treatment.
The parallels to other infrastructure maturations are worth noting. Just as Streamline Your Monorepo: Changesets v3 Simplifies Releases and Reduces Size shows how shared tooling reduces friction in monorepos, a shared LLM platform reduces friction in agent behavior. And the same principle that drives Reduce Search Result Tokens by 74% with Smarter Data Structuring, that structured inputs cut noise and cost, applies directly to schema enforcement in agent outputs. In both cases, the fix is not more clever prompting but better architectural hygiene.
The practical takeaway is straightforward: if your organization is building more than one AI agent, you already need a shared LLM platform. The cost of not having one is not just wasted tokens or occasional hallucinations. It is the slow erosion of trust in your systems. A prompt registry alone prevents the silent drift that turns a working recommendation engine into a liability. Schema enforcement ensures that what the model says translates cleanly into what your database expects. And token cost attribution turns an opaque expense into a measurable input per user action. The question every team should ask is not whether their current agent works today, but what happens when the prompt changes next quarter and nobody remembers who wrote it.