The operational layer around production AI
Shipping an agent is the start of the work, not the end of it. Once it runs against real traffic, three questions decide whether it holds: what it costs, what it can reach when it goes wrong, and whether it stays up under load.
The sections below cover the mechanisms we put in place for each one, and a project where they ran in production.
Financial Operations
The bill arrives before anyone has agreed on what it should be
An invoice nobody can explain: a number that climbs faster than usage, with no way to say which workflow or which agent drove it. Token and inference spend stays hidden until the month closes, and by then a single loop can have run for days.
The mechanisms make the spend legible and bounded before it lands:
- Token and inference cost attribution. Spend tracked per workflow, per agent, and per tier, so the number on the invoice maps back to the thing that caused it.
- Budget caps. Hard limits per agent and per workflow that stop a run when it crosses the line, rather than letting it spend into open-ended territory.
- Semantic caching. Repeated and near-repeated requests served from cache instead of a fresh inference call, cutting cost on the work that does not need a fresh answer.
- Runaway-loop monitoring. Detection that catches an agent stuck in a loop and trips a cap before it shows up on the bill.
Risk Management
An agent with broad access is one bad decision away from real damage
Blast radius: an autonomous agent that can reach a production database, a customer's data, or a payment path, and acts on its own judgment when that judgment is wrong. A mistake is survivable. A mistake with write access to any of them is not.
The mechanisms bound what an agent can do and put a person on the decisions that matter:
- Runtime action management. Each action an agent attempts is allowed, denied, or deferred at runtime against an explicit policy, rather than trusting the agent to stay inside the lines on its own.
- Sandboxing. Agents run in an isolated environment with a constrained view of the filesystem and network, so an action that goes wrong is contained.
- Least-privilege agent identity. Each agent gets its own identity scoped to exactly the resources it needs, so a compromised or confused agent cannot reach past its job.
- Human-in-the-loop gates as policy-as-code. The decisions that require judgment are deferred to a person, expressed as policy the platform enforces rather than a convention people are asked to remember.
Platform Reliability
A long-running agent that dies mid-task should not start over from nothing
A multi-step run that fails halfway leaves you guessing whether it half-finished and whether you can safely run it again. A stateful agent holds context across many steps, so a crash loses work a stateless request would not have accumulated.
The mechanisms make a run recoverable and keep a regression from reaching production:
- Durable execution with checkpointed resume. Progress is written to checkpoints as the run proceeds, so a process that dies mid-task resumes from where it left off instead of replaying from the start.
- Progressive delivery for stateful agents. New agent versions roll out to a slice of traffic first, with the state-handling watched before the rollout widens.
- Eval harnesses as regression gates. A suite of evals runs against each change and blocks a release that regresses behavior, so reliability is checked before deploy rather than discovered after.
Have agents in production already?
Tell us what you're running and where it's straining. We'll give you an honest read on which of these layers to put in place first.