01
Process
The steps, states, handoffs, deadlines, approvals, and recovery paths that define how work actually moves.
§ 02 — Why the wall exists
AI doesn’t graduate to production
without the scaffolding
production requires.
A model can reason. It cannot, on its own, take ownership of a case, follow a defined process, escalate to the right human, get reviewed, get versioned, or get audited. Without that scaffolding, your AI cannot ship into the operation. With it, the same model becomes a working teammate.
See the scaffolding →
AI teammates succeed the same way human teammates do. They need a defined role in a defined process.
§ 03 — What changes
Turn your AI investment into operating capacity.
With that structure in place, AI stops being a pilot and starts being capacity. Your team runs more cases, closes more files, serves more customers, under the same rules they already work by.
- 01Production AI that pays back the investment.
- 02Agents take roles in your process: reviewing submissions, validating policies, coordinating approvals. Case volume goes up. Headcount stays flat.
- 03Audit, ceiling, and brake built in, so AI ships instead of stalling in legal review.
Production AI is the new competitive advantage.
Verify the agent before
you trust it to run.
An agent earns the right to run the way a teammate does — by proving itself first. Regisseur verifies every agent on the axes that decide whether it can be trusted, then lets it run only as far as the proof allows.
Proven before it runsSee the eval suite →
- AccuracyGraded against expected outputs and rubrics.
- PerformanceLatency measured at p50 / p95.
- Cost$/run — the cheapest model that still passes.
- ReliabilityRated against a saved baseline; drift can block the change.
01Verify
You can prove what it did.
Every agent action is bound before it runs, watched while it runs, and replayable after. You can show exactly what happened — and prove it stayed inside its limits.
How it’s governed →02Autonomy
So you can let it run.
What you can verify, you stop reviewing by hand. Raise each agent’s autonomy ceiling as high as the proof allows — not as high as you hope — and take the human off the steps that no longer need one.
The operator surface →03Return
That’s where the ROI is.
Leverage comes from the work agents do without you in the loop. An agent you have to watch saves nothing. The return scales with how high you can safely set the ceiling.
See it running →AI agents need a stage. Every stage needs a Regisseur.
A stage manager keeps every performer and every cue in sequence. Regisseur is the System of Action where AI agents and humans turn operational signals into assigned, executed, reviewed, and recorded work.
§ 05 — The missing layer
Records hold what happened. Insights show what matters. Regisseur acts.
SYSTEM OF ACTION
The governed surface where signals become assigned, executed, reviewed, and recorded work.
ProcessToolsAgentsIntegrations
SYSTEMS OF RECORD
Authoritative operational history. Unchanged, not replaced.
ERPCRMTMSDocument storeCore database
SYSTEMS OF INSIGHT
Analytics, forecasts, scores, and signals that inform the next move.
BIAnalyticsForecastingMarket dataScorecards
§ 06 — How it works
Four configurable primitives. One operating surface.
02
Tools
The bounded capabilities agents and humans can call: read, write, transform, notify, sign, escalate, or verify.
03
Agents
AI teammates with versions, ceilings, input contracts, output schemas, and explicit ownership inside the process — each with an eval suite that proves it works on the cheapest model that still passes.
04
Integrations
Connections to systems of record through scoped provider and MCP contracts — not unbounded access.
§ 07 — Where the work happens
Where the team and the process come together.
Every case has a process on the left and a team working it on the right. Some teammates are people. Some are agents. They share the same record, the same rules, and the same next decision.
Read the operational reality →
§ 08 — What the platform does
The team, the process, and the next decision stay in one frame.
In McKinsey’s terms, this is the split between functional agents that run domain tasks and enterprise agents that oversee work end to end — and flag low-confidence decisions for human intervention. McKinsey, The AI assembly line (May 2026)
01Team Layer
Coordinates the team
Humans and agents share ownership of the same operational process. Every task has an accountable owner, an autonomy ceiling, and an escalation path.
02Execution Layer
Executes deterministically
The process runs as typed, repeatable steps. AI reasoning is one bounded tool inside the process, not the process itself.
03Decision Layer
Surfaces the next decision
The next action arrives with context, confidence, lineage, and the reason it needs a human or agent owner. The why travels with the work.
§ 09 — The category, validated
McKinsey named the orchestration layer. Regisseur ships it for regulated industries.
In The AI assembly line, McKinsey describes the enterprise’s target state as an agentic orchestration layer — one that “acts as the conveyor belt … managing the flow of tasks, decisions, and data across the organization.” Regisseur is that layer — built with the audit trail, autonomy ceilings, and human gates a regulated enterprise needs to put it into production.
McKinsey & Company, The AI assembly line: Strategic imperatives for CEOs (May 2026)
Runs in your boundary
Your keys. PHI masked. Data never leaves your environment.
McKinsey lists anonymizing data before model input among enterprise requirements; in Regisseur it is the default deployment, not an add-on.
§ 10 — Safe to deploy
Seven structural guarantees. Before the first process goes live.
S.01
Audit trail on every decision
Reconstruct any work item — what happened, who decided, on which version of the rules.
S.02
Regulatory versioning
Past work remains re-runnable on the exact forms and agents that were live when the process started — even after rules change.
S.03
Configurable autonomy ceilings
Every agent operates within a ceiling set by operations — always review, graduated, or fully autonomous. The agent cannot exceed it.
S.04
Deterministic processes
Agents run typed, repeatable process steps. AI reasoning is one step — not the driver.
S.05
Workspace-wide emergency brake
One action pauses every agent, queue, and outbound message across the platform.
S.06
Configurable without engineers
Process managers build and adjust operational processes in conversation — not sprints.
S.07
Rehearsed before trusted
Run any agent against a real scenario with the outside world mocked, and inspect every step before it goes live. Optionally gate publishing on a passing eval — a draft can’t ship until it proves itself against that exact version.
You never trust the AI. You bound it with deterministic boundaries — then replay exactly what it did.
Govern before, verify after →§ 11 — Test before trust
Test the work before the work is real.
Every Regisseur agent runs as a deterministic pipeline — the same inputs take the same path every time. So you can run an agent against a real scenario with the outside world mocked, and watch the whole decision unfold before you let it act.
Pick a scenario. Run the agent once. The outside world is stubbed — no email leaves the building, no record is written, no external system is called — but the agent reasons for real. You get back the full trajectory: every step it took, every tool it reached for, which branch it chose, and what each step returned. Mock any step’s result yourself to rehearse the unhappy paths you can’t trigger on demand — the rejected claim, the missing document, the check that fails.
When you’re ready to ship, turn on the eval gate. Now a draft can’t go live until it passes its tests — and the pass has to be for that exact draft, not an older version that once worked. The quality bar becomes part of deploying, not a separate step someone remembers to do.
01
Dry-run, for real
Run a live scenario with no side effects — no emails, no writes, no API calls. The agent reasons exactly as it would in production; only its outward actions are stubbed.
02
Watch the trajectory
A step-by-step trace of what the agent decided and why — each step’s type, the tool it called, the branch it took, the output it produced. Nothing hidden in a black box.
03
Mock the world
Stub any step’s result to rehearse the paths you can’t trigger on demand — the rejection, the missing field, the failed check — before a customer hits it.
04
Gate the deploy
Switch on the eval gate and a draft can’t publish until it passes — verified against that exact draft, not a previous version. Tested before trusted.
RPA records a click. Regisseur lets you rehearse the entire decision — and blocks the ones that fail.
§ 12 — Why the scaffolding matters
RPA breaks on judgment. LLMs break on accountability.
RPA cannot adapt when the work requires judgment. Agent frameworks can reason, but they do not provide causal audit, runtime compliance, autonomy ceilings, or an emergency brake. Regisseur is the production scaffold for operating AI in real workflows.
| Approach | Cognitive adaptability | Causal audit | Runtime compliance | Autonomy ceiling | Emergency brake |
|---|---|---|---|---|---|
| Legacy RPA | None | Logs only | Offline policy | N/A | Manual |
| LLM-on-RPA bolt-on | Yes | Opaque | Policy drift | No | No |
| Agent framework | Yes | Tool logs | Developer effort | Prompt-level | No |
| Regisseur | Yes | Append-only · replayable | Tier 1 · enforced | Engine-level · earned | Workspace-wide |
LangChain builds the agent. Regisseur is the production scaffolding required to operate it. For a deeper comparison with LangChain specifically →
§ 13 — Capabilities, in action
Five demos. One engine.
Regisseur runs the same way whether the process is months long or minutes long, regulated or operational, triggered by a phone call or a webhook. Five operations we can walk through live, in one workspace.
Demo walkthrough: life insurance underwriting
Regulated decisioning, end to end
Application intake through APS, lab analysis, underwriting summary, and policy issuance. Every underwriting decision reviewed by a human. Everything between them runs as a chain of agents under explicit autonomy ceilings.
Human gateMedical review, underwriting approval, policy issuance.
SystemsDocuSeal · external party portals · custom underwriting forms.
Demo walkthrough: enterprise employee onboarding · HRIS-triggered
Cross-system onboarding, zero tickets
A new hire record in the HRIS becomes a provisioned, welcomed, closed-out employee in minutes. Six agents chained: intake, access planning, equipment provisioning, IT access, welcome package, HRIS writeback. One manager review on the access plan; everything else autonomous.
Human gateManager approval on the access plan.
SystemsHRIS · Active Directory · Azure AD · IT asset management.
Demo walkthrough: employee offboarding · HRIS-triggered
Departure closure, no loose ends
An HRIS termination event becomes a fully revoked, closed-out departure. HRIS intake → IT footprint analysis → access revocation across AD, Azure, and applications → M365 data transfer → compliance review → HRIS writeback.
Human gateIT footprint review. Compliance sign-off before HRIS writeback.
SystemsHRIS · Active Directory · Azure AD · M365.
Demo walkthrough: IT access requests · ServiceNow-triggered
Compliance-bounded approvals
A ServiceNow ticket becomes a structured policy decision. An agent reads the request, fetches the user's AD profile, reasons against SOX and ISO 27001, and hands the manager a recommendation before they ever see the raw ticket. After approval, provisioning runs across AD, Azure AD, and the target application simultaneously.
Human gateManager approval, with policy reasoning pre-attached.
SystemsServiceNow · Active Directory · Azure AD.
Demo walkthrough: IT incident management · ServiceNow-triggered
Plan-before-action incident response
A ServiceNow incident is classified against the CMDB and knowledge base. A remediation plan is built and reviewed by a tech lead before anything changes in production. Once approved, remediation runs through Intune and the closure writes back to ServiceNow. Mean time to resolution drops because diagnostics happen while the human reviews a finished plan.
Human gateTech lead approves the remediation plan before execution.
SystemsServiceNow · CMDB · Intune.
Five operations. Same primitives. The vertical is configuration.
§ 14 — Deployment-ready operations
Not just configurable. Deployable, testable, and promotable.
D.01
Workspace providers
Mail, messaging, signing, and error reporting resolve per workspace. Admins test connections; every write is ledgered.
D.02
Signing as a process pause
A process can halt on an external signature, render a token-gated signing portal, then resume the cascade on completion.
D.03
On-prem capable data plane
Local Postgres, storage, queue, and DocuSeal support regulated evaluations without a hosted product data plane.
D.04
Signed vertical bundles
Workflow templates, agents, forms, documents, and tools move between workspaces as audited operational packages.
The product story now extends past orchestration: Regisseur can configure the workspace, prove the provider path, and package the vertical.
See the deployment model →§ 15 — How the work splits
Seamless human–AI teams. Bounded by design.
Agent team · Humans
Judgment & escalations
- FocusComplex work
- OwnsOperational outcomes
- WorkExceptions · approvals · decisions
- SpeedValue-added · no re-keying
AI team · Bounded agents
Orchestration & follow-up
- OrchestratesFollow-up loops and tool calls
- ApprovalsMandatory human sign-off where required
- FlagsLow-confidence → human owner
- NeverOverrides autonomy ceiling
§ 16 — Start a workspace
A 45-minute walkthrough.
Your process, configured as a team.
Bring one operational process with human judgment, system data, and repeatable follow-up. We’ll map the process, the tools, the agents, and the first review gates.