Most AI infrastructure still asks teams to think in ghosts.
When workflows are framed as personalities with memory, judgment, and collaboration, teams stop designing around explicit state, control flow, validation, and reproducible behavior. The system becomes harder to test, harder to compare, and harder to change safely. Control surfaces get fuzzier. Small prompt changes affect too much at once. Evals drift toward vibes. Debugging becomes interpretation instead of procedure.
The result is familiar: brittle workflows, silent regressions, manual QA, and production changes that feel riskier than they should.
Ockham is built on a simple rule: don't replace systems engineering with ghost stories. Treat AI workflows as systems to control, test, replay, and verify, not personalities to coax, interpret, and hope behave.
Ockham can work with the protocols without inheriting the ghost story.
MCP endpoints are tool nodes. A2A services
are external nodes with capability-based routing. Agent Cards are
service descriptors. Tasks are RPC calls with a lifecycle.
Ockham consumes the protocols. It doesn't buy the ontology.
These are not new problems. We inherit from the infrastructure that already solves them and do not pretend otherwise.
The novel surface is narrow and honest: replayable workflow evidence, reusable regression cases from real runs, and safer prompt, model, and workflow changes in production. Everything else is borrowed. That's the point.
Because the orchestration layer shouldn't be the slow part. Because strict type systems prevent the category errors we're trying to eliminate. Because infrastructure that outlasts hype cycles should be built in a language designed to outlast hype cycles.
If you're building production AI workflows and want to compare notes, reach out at inbox@robertdberry.com.