New build
You have a workflow in mind.
A real business process (claims triage, customer support, sales ops, data extraction) that needs an agent doing actual work. We build it with the governance the work demands.
Offering · Agent Engineering
Build agents that do the work. Govern them so they keep doing it.
Production AI agents built on four pillars: Policy Enforcement, Zero-Trust Identity, Sandbox Evaluation, and Reliability Engineering. The discipline behind this is Operational Systems Engineering.
Who It's For
New build
A real business process (claims triage, customer support, sales ops, data extraction) that needs an agent doing actual work. We build it with the governance the work demands.
Stuck
You bought an off-the-shelf agent or wired one up with a no-code tool, and it can't integrate, can't be governed, or can't be audited. We replace or wrap it.
Shipped & exposed
The audit surfaced what's missing: policy gaps, identity sprawl, no sandbox, no SLOs. This is where those gaps get closed.
The Four Pillars
An agent that "works" in a demo is a workflow. An agent that's allowed to keep working in production is a governed system. We build for the second.
01 · Policy Enforcement
Programmatic guardrails at the tool boundary: schema validation, action policies, refusal patterns, rate limits, content filters. Not "we asked the model nicely." The contract is in code, not in the prompt.
Built around OPA-style policy where it fits, typed-tool contracts via MCP everywhere else. An agent that can read the CRM shouldn't be able to delete records. Obvious. Enforced.
02 · Zero-Trust Identity
Per-agent identity, scoped credentials, time-boxed access. MCP tools authenticate as the agent, not as the user or a shared service account. No long-lived secrets in prompts or env vars. If an agent leaks, the exposure is one agent, not the org.
Workload identity (cloud-native), short-lived tokens, role assumption per tool call. The audit trail says which agent took the action, not "the AI did."
03 · Sandbox Evaluation
Golden datasets, automated evals, regression gates, pre-merge runs. An isolated environment where new prompts, new tools, new models get probed before they touch real users. Drift gets caught here, not after a customer notices.
Langfuse for traces, custom harness for evals tied to your workflow. Adversarial cases (injection, jailbreak, ambiguous intent) graded automatically. CI blocks the deploy if evals regress, not only if tests fail.
04 · Reliability Engineering
Service Level Objectives (SLOs), error budgets, drift detection, cost ceilings, on-call rotation. The discipline between a system that runs and a team that operates it. Production agents are infrastructure, not magic.
Latency P50/P95/P99 budgets per workflow. Cost per request, alerted on regression. Retry, timeout, fallback paths. Runbooks for the failures we've seen before, and a known way to escalate the ones we haven't.
The Workflows Side
The pillars are the build standard. The workflow is the brief. We start from the process you have (claims triage, support deflection, sales ops, internal Q&A, data extraction) and architect the agent for that specifically. Generic chatbot scaffolds don't ship.
Why Us
The four pillars aren't theoretical. ContinuumState runs production agents on this discipline every day. Fasten (open-source, Apache-2.0) is the audit substrate we ship into client builds. It's the same tamper-evidence layer the AI Decision Audit relies on. Same toolchain on both sides of the engagement.
If you started with an AI Decision Audit, this is where the findings turn into fixes.
Tell us the workflow. We'll come back with a scoped plan: policy model, identity boundary, eval strategy, reliability budget, and timeline.