Architecting a production grade AI execution engine for enterprise operations.
Large operational workflows like approvals, data enrichment, and multi-system updates were being handled manually across disconnected tools. I designed and led the build of an execution engine that could run these workflows reliably at scale, with AI handling the reasoning and deterministic layers handling everything that needed to be correct every time.
The core architectural decision was treating AI as a planner rather than an executor. The model decides what needs to happen. Separate deterministic layers carry it out, validate the result, and surface it for human review where the risk warrants it. This made the system auditable, testable, and recoverable in ways a single model call never could be.
Designing for failure from the start
Every stage in the pipeline could fail independently. I introduced per-step retries, fallback paths, and a dead letter queue for workflows that exhausted their attempts. Operations could inspect and replay any failed run without engineering involvement.
A tool abstraction the model could reason about
Rather than giving the model raw API access, I designed a tool registry with typed inputs, output contracts, and explicit side effect declarations. This made it possible to unit test every tool in isolation and gave the planner enough context to sequence them correctly.
Validation as a first class layer
The validation layer ran schema checks, business rule assertions, and confidence thresholds before any output reached a user or triggered a write. When something fell outside acceptable bounds it was routed for review rather than silently passed through.
A review interface built for throughput
Human review was only useful if it was fast. I built the approval UI around keyboard navigation and batch actions so reviewers could process a high volume of flagged workflows without the interface becoming the bottleneck.
A tighter integration between the model and execution layer would have been faster to ship. The separation added surface area and required more upfront design. That investment paid off when we needed to swap model providers mid-project without touching the execution logic, and again when the validation layer caught a class of model errors that would have silently corrupted downstream records.
The engine processed thousands of workflows a month across multiple business functions with a failure rate under one percent. Teams that previously spent days on manual processing handed the work off entirely. The architecture was later adopted as the internal standard for any new AI feature going into production.