Agents That Get Past the Security Review
Enterprise teams ship agent demos that never reach a real workflow. 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.
Agents We Put into Production
Common governed agents we take live on the execution layer, each cited and auditable.
Legal
Litigation and compliance
From matter intake to contract close, governed AI agents do the document-heavy work, cite every source, and hand off to lawyers who decide.
Explore Legal36 use cases
Litigation document review
Review large litigation document sets for relevance, issues, and risk, with every call cited and logged.
Contract review and redlining
Check contracts against your playbook, flag risky terms, and propose redlines for a lawyer to approve.
Clause extraction and comparison
Pull key clauses across a contract set and compare them side by side, each linked to the source.
Deposition and transcript analysis
Turn long transcripts into searchable summaries, key admissions, and contradictions, cited to the line.
Case-law and regulatory research
Research case law and regulation on point, with sources cited so a lawyer can verify every claim.
Privilege review
Flag potentially privileged material across a document set, with reasons and a clear audit trail.
eDiscovery culling
Cut review volume by culling irrelevant material early, with every decision logged and reversible.
Due-diligence data rooms
Read an entire data room and surface the obligations, risks, and gaps a deal team needs.
Matter intake and timeline intelligence
Turn a full matter file into a structured, source-cited Timeline Brief, with conflicts checked and gaps flagged before the first strategy meeting.
Obligation and renewal tracking
Track contractual obligations and renewal dates across your agreements, with alerts before they lapse.
Brief and memo drafting
Draft briefs and memos from your record and authority, cited throughout for a lawyer to finalize.
Citation checking
Check every citation in a document for accuracy and support, and flag anything that does not hold.
Compliance policy QA
Answer policy and compliance questions from your own documents, with the source cited every time.
Contract generation
Generate first-draft agreements from your templates and approved clauses, ready for a lawyer to review.
Regulatory filing review
Check filings against requirements before they go out, and flag gaps for a person to resolve.
Chronology and fact reconstruction
Build a source-cited timeline from mixed matter files and flag the evidentiary gaps a lawyer needs to close.
Witness statement preparation
Draft witness statements grounded in the record, with every assertion cited for a lawyer to verify and own.
Demand and response letter drafting
Draft demand and response letters from the record and your templates, ready for a lawyer to finalize.
NDA and standard agreement processing
Process high-volume NDAs and standard agreements against your playbook, with every deviation flagged and logged.
Legislative and regulatory monitoring
Monitor legislative and regulatory sources for changes that touch your matters, each surfaced with a citation.
Precedent and playbook building
Turn your past work into a searchable precedent bank and keep playbook positions current, each linked to its source.
Risk and issues spotting
Read a document set and surface the legal risks and open issues a lawyer should look at first, each cited.
AML and KYC document processing
Process AML and KYC documents against your requirements, flag gaps and discrepancies, and log every check.
Regulatory change impact analysis
Map a regulatory change to the policies, contracts, and obligations it touches, each finding cited for review.
Arbitration record building
Assemble the arbitration record from filings and exhibits into a cited, navigable set for the team to rely on.
Expert report analysis
Analyze expert reports for assumptions, methods, and weak points, each cited for counsel to probe.
Tribunal submission drafting
Draft tribunal submissions from the record and authority, cited throughout for counsel to finalize.
Multi-jurisdiction regulatory mapping
Map how a requirement differs across jurisdictions and surface the conflicts a lawyer needs to resolve, each cited.
Employment agreement generation and sending
Generate employment agreements from your templates and approved terms, ready for legal to approve before they send.
Offer letter automation
Generate offer letters from approved templates and role data, ready for an owner to review and release.
Annual increment and compensation amendment tracking
Generate and track increment letters and compensation amendments, with every change logged for review.
ESOP and equity agreement management
Generate and track ESOP and equity agreements against approved grant terms, with every action logged.
NDA and confidentiality agreement workflows
Run the NDA and confidentiality agreement lifecycle from draft to signature, with every step logged.
Termination and separation agreement drafting
Draft termination and separation agreements from approved terms, ready for legal to review and own.
HR policy QA and compliance review
Answer HR policy questions and check workforce documents against policy, with the source cited every time.
Joining and onboarding document processing
Process joining and onboarding paperwork against your checklist, flag what is missing, and log every step.
Where Agent Projects Actually Stall
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.
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.