Autonomous agents that do the work — governed and safe.
Real autonomous agents that plan, use tools, and act on your systems under deterministic guardrails — powered by our Unified Agentic Platform (UAP). Not AI-assisted development.
Most "AI" today assists a human who still does the work. The step change is autonomous agents that own a task end to end — but only if they are governed, observable, and safe enough to trust with real actions.
If a few of these sound familiar, this is the work to start.
- 01Your 'AI' still needs a human to do the actual work.
- 02You want agents to take real actions but cannot yet trust them to.
- 03Prototypes shine in demos but have no path to safe production.
- 04Every team is rebuilding the same agent plumbing from scratch.
What we do
Unified Agentic Platform (UAP)
A shared SDK for agents: base LangGraph agent framework, model routing, RAG, session, messaging, and a full lifecycle/governance layer — teams ship a new agent in hours.
Deterministic guardrails
Non-LLM policy evaluators — policy ceilings, auth, PII, conflict, escalation, tenant permission — return pass / warn / block before any action executes.
Human-in-the-loop approvals
High-risk actions pause asynchronously for human approval, with the full plan trace and blast-radius score attached.
Governed at scale
Catalog, versioning, promotion, certification, and full traceability — so many agents can serve many tenants safely.
A contact-center team wants an agent to resolve billing issues, not just draft replies. We build it on UAP: it plans, retrieves account context, and proposes a fix — but a deterministic guardrail checks the credit against a policy ceiling, and anything above the threshold pauses for a supervisor with the full trace attached. The agent handles the routine end to end; humans stay in control of the risky.
Illustrative scenario — not a specific client.
Pick a task an agent can own end to end, with a clear success and safety definition.
Build it on UAP: nodes, tools, and policies specific to your domain.
Prove it in shadow mode against real work, guardrails enforcing throughout.
Promote to production with approvals, tracing, and lifecycle governance.
Concrete artifacts, not a slide deck.
- Agents built on UAP that own a task end to end.
- Deterministic guardrails returning pass / warn / block before any action.
- A human-in-the-loop approval queue for high-risk steps.
- Full plan traces and blast-radius scoring for every run.
- Lifecycle governance — catalog, versioning, promotion, certification.
Good questions.
How is this different from a copilot?
A copilot assists a human who still does the work. Our agents own the task end to end, taking real actions under guardrails, with humans approving only the high-risk steps.
How do you stop an agent doing something harmful?
Deterministic, non-LLM guardrails evaluate every action before it executes and can block it outright; high-risk actions pause for human approval. Nothing touches your systems unchecked.
Do we have to adopt the whole platform?
You start with one task an agent can own safely. UAP provides the shared plumbing so that first agent — and the next — ship in hours, not months.