Building Agents is the Easy Part. Running Them in Production is the Job.
Enterprise teams ship agent demos that stall at the security review. CreateOS forward-deployed engineers take your agents to governed production on the unified AI execution layer: every step planned, scoped to an autonomy level, policy-checked, validated, and audited.
- ISO 27001 and SOC 2 Type II certified
- Single agents or coordinated teams
- Autonomy controls on every agent
- Governed and auditable by default
The Gap is Production, Not the Model
Agents demo well and stall at the security review. Point tools stop at routing and eval tools stop at observability. CreateOS unifies routing, governance, output validation, and observability in one path, which is what closes the gap.
of enterprise AI pilots never reach production.
MIT NANDA, 2025
of enterprises already run AI agents, then hit the infrastructure wall.
McKinsey, 2025
agents is where coordination breaks without a system underneath. Orchestrators in production average ~12.
HFS Research, 2025
What We Deliver
Agents built for production from the first call, not a demo that stalls at security review.
Single-agent build
We scope, build, and ship one agent: the task defined, the tools wired, the prompt hardened, and the output validated before it touches a real workflow.
Multi-agent orchestration and handoff
Coordinated agent teams that plan, delegate, and hand off cleanly. Routing decisions are logged, handoffs are explicit, and no step is a black box.
Autonomy controls and approval gates
Every agent is scoped to an autonomy level. High-stakes steps pause for a human decision. Per-agent budgets and sandboxing keep risk contained.
Tool and system integration
Agents connect to your systems of record, APIs, document stores, and data sources. Integration is governed and access is scoped to least privilege.
Governance and output validation
Policy enforcement, hallucination checks, and PII masking run on every agent response before it reaches a user or downstream system.
Observability and audit trail
Every agent call is logged with execution traces, decision lineage, and a full audit record, so a security or compliance team can inspect any run.
How an Engagement Works: The Production Path
A staged path from concept to governed production. Value lands early and governance holds at every step.
- 01
Discover
We pick the highest-impact agent use case, scope it, and produce a build spec and production roadmap. Fixed pricing agreed in writing.
- 02
Prove
We stand up a scoped agent pilot on the execution layer, governed from the first call, to prove value against your own data.
- 03
Productionize
Forward-deployed engineers harden it: routing, policy enforcement, output validation, autonomy controls, and a full audit trail.
- 04
Scale
It goes live, then spreads. Model lifecycle management, monitoring, and improvement on the layer you keep.
Proof: a litigation agent in production
CreateOS built a litigation intelligence agent that turns raw matter files into a source-cited Timeline Brief before the first strategy meeting. The agent reads mixed document types in full, contracts, notices, emails, handwritten notes, and scanned annexures, extracts the dozen clauses that bear on the live question from a 400+ page contract, and builds a chronology with every entry linked to its source page. Forward-deployed engineering on the governed execution layer, deployed to CreateOS cloud, firm environment, or fully on-premise.
Less manual Timeline Brief preparation time, subject to matter complexity and document quality.
Relevant clauses surfaced from a 400+ page contract, each with surrounding context preserved.
Deployment modes: CreateOS cloud, firm environment, or fully on-premise, with zero data retention by default.
Agents We Put into Production
Common governed agents we take live on the execution layer, each cited and auditable.
Litigation document review
Reads large document sets for relevance, issues, and risk. Every call is cited and logged. A lawyer reviews before anything is relied on.
Contract review and redlining
Checks contracts against your playbook, flags risky terms, and proposes redlines for a lawyer to approve. Every edit is logged.
Fraud-alert investigation
Gathers the transactions, account history, and prior cases behind each alert, scores it against policy, and hands an analyst a decision-ready file.
AML alert triage
Structures AML alerts, attaches supporting transactions, and proposes a disposition with reasoning. An analyst confirms and the full trail lands in the case record.
KYC document intelligence
Reads onboarding packets in full, extracts what compliance needs, checks completeness against your checklist, and flags gaps before review.
Customer request resolution
Resolves routine requests with governed, policy-checked responses. PII is masked before anything is shown. Complex cases go to a person with full context.
Multi-agent financial analysis pipelines
Coordinated agent teams across financial analysis, memo drafting, and approvals. Every figure is traced to its source and every handoff is logged.
Support agents with full lifecycle resolution
Resolves the complete ticket lifecycle with governed responses and a clean handoff to a person when an agent should not answer.
Policy knowledge assistants
Answers questions grounded in approved internal sources, scoped per role. Where policy is silent, the agent says so. Every query is logged.
Why CreateOS for AI Agents
Governed from day one
Policy enforcement, output validation, and a full audit trail are on from the first call, not bolted on before the security review.
Engineers who ship onto a platform you keep
Forward-deployed engineers embed with your team and ship onto the unified AI execution layer we operate. The engagement ends; the governed layer stays.
Autonomy you control
Every agent is scoped to an autonomy level with approval gates and per-agent budgets. You can see, stop, scope, or sandbox any agent.
You own everything
All code, models, and IP are yours outright. We document everything and train your team.
Common Questions
What does an AI agents engagement with CreateOS cost?
Engagements run on fixed-scope pricing, not hourly retainers. A discovery sprint and first pilot scope is agreed in writing before any build begins. Larger ongoing builds run on milestone-based contracts. Cost depends on the number of agents, integration depth, and deployment mode.
How long does it take to get an agent to production?
The standard rollout is 12 weeks across three gated phases of escalating autonomy. The fastest comparable deployment went from pilot to full production in 75 days. Simpler use cases can go live in under two weeks.
We already built agents. Can you take them to production?
Yes, that is the most common engagement. Bring agents from any framework or builder. We wire them into the governed path, add the policy enforcement and audit trail your security team requires, and stand them up in your environment.
Who owns the IP after the engagement?
All code, models, data pipelines, and IP are yours outright. We document everything and train your internal team to manage what has been built.
Where does our data live and how is it protected?
In your environment. CreateOS runs in your VPC or on-premise, with region-locked compute and zero data retention by default, so regulated data never crosses a border you did not approve. All agents operate under policy enforcement and access controls from the first call.
How do you prevent agents from hallucinating or leaking data?
Output validation, hallucination checks, and PII masking run on every agent response before it reaches a user. Access is scoped to least privilege, and every call is logged with an audit trail.
What makes this different from hiring an AI consultancy or building in-house?
A consultancy hands you a build on tools it does not control and leaves. CreateOS delivers on the execution layer we operate ourselves, so routing, governance, output validation, and audit stay enforced after the engineers roll off. Building in-house means staffing for context engineering, evaluation, and ongoing governance work that most teams underestimate. The governed layer stays after the engagement ends.
Where do you want to start?
Bring one stuck agent pilot. We will take it to governed production on the execution layer.