Where transformation work runs
Enterprise transformation needs a platform to become agentic.
A transformation question takes days or weeks the usual way, spans live systems, and ends in a decision someone has to own. Generic agents were built for a turn. This platform was built for the long run.
How it is composed
Four layers, from your systems to a decision
Studio sits above the architecture, delivery, and service tools you already run. Each layer has a job on the way from a question to an artifact you can decide from.
Outcome
Decisions
Walk in and win the room. The options, the evidence, and the call are already on the artifact.
Canvas
Practice
Foundation
Harness · the foundation
Hours of analysis, still under your control
Retirement impact, sequencing risk, dependency exposure — those questions take days or weeks of reconciliation the usual way. A chatbot cannot run that work. The Studio Harness can: the agent plans the approach, carries long-running tasks, and stays steerable through hours of analysis until you can decide on the canvas.
Studio Harness
Runtime
The Studio Harness loop: deep work you control. Pause, resume, or redirect the same job.
Planning
Draft the approach first. Keep a goal for long work. You approve, adjust, or send it back before the agent acts.
Session
Come back to the same run. The work is still there, ready to continue.
Approvals
Reads and views run. Writes arrive with a preview and wait. The agent can stop and ask you.
Tools
The agent reaches the systems the question depends on, keeps the analysis moving, and stops when a trade-off needs you.
Delegation
Hand a slice to a sub-agent. It reports back; you stay with the original work.
Plan first
The agent can draft the approach, keep a goal for the long work, and wait. You approve, adjust, or send it back before it acts.
A session that continues
Come back to the same run. The Studio Harness keeps the work in motion.
Gates where they matter
Reading and viewing run. Writes arrive with a preview and wait. The agent can stop and ask you a question.
Steer mid-run
Pause, resume, or redirect the same job. A follow-up does not start a new proposal from zero.
Delegate a slice
Hand an independent cut to a sub-agent. It works that slice and reports back; you stay with the original work.
Work you can see
Every tool call lands on the timeline. You can watch the analysis, not only the finished artifact.
Skills and tools · the method
Your house method, and the tools that carry it
Every team runs transformation differently: how an impact question gets scoped, how a steering artifact gets drafted, how options get framed for a decision. Skills write that method down as playbooks. Tools and visual apps come from the sources you attach. Agents load both as the work calls for them.
Repeatable
The same method runs the same way across agents, sessions, and engagements.
Tools from your sources
Connections supply retrieve-and-act tools and the visual apps that land on the Stage. Skills decide how those tools get used.
Loaded as the work calls
Playbooks enter a run progressively, so agents carry your method without drowning in it.
Assigned on purpose
Depth comes from deliberate assignment to the agents that need each playbook and tool, not automatic noise.
Owned by your organization
Your library, your edits, reused by every agent you run.
Agents · the capacity
Analyst-grade capacity, briefed on your estate
Every transformation question begins with reconciliation: an initiative here, a capability there, a service somewhere else, all pointing at the same change. Studio agents are configured for that job before they ever run: what they know, which playbooks they carry, which sources they may read, and the limits they work under.
Briefed before they speak
Instructions, playbooks, allowed sources and tools, and working limits are set in advance. Selecting the agent is all the setup a run needs.
Least privilege by default
Each agent sees only the sources and tools allowed for it, connection by connection.
Self-describing runs
Every run records its model, skills, and sources, so any answer traces back to its inputs.
Consistent by construction
The same agent works the same way tomorrow, across sessions, teams, and client engagements.
Models chosen per conversation
Bring your own providers. Pin a default model to an agent or pick one per thread. You keep the contract and the spend.
Definitions you can import
Standard agent cards stand up new teammates fast, ready for your team to tune.
Artifacts · on the Stage
Impress the room — then stay on the data
You and the agents co-create on the Stage. Studio compiles the artifact; it does not paste it. What lands is grounded in the records the work retrieved and the changes you accepted — so when they push back in the room, you are still on the data.
Built from connected data
Every card and bar can show the system and record it came from.
Interactive to the core
Focus, filter, drill down, compare versions, and edit alongside the agent in the same artifact.
Version history that means something
Compare any two drafts side by side; older versions stay readable forever.
Board-pack ready
Present mode goes full screen with no rework.
Reopened against today
Artifacts re-render from their stored data, so last week's artifact opens with today's data — provenance still on it.
Organized for reuse
Pin favorites, tag by engagement or domain, filter by family: the gallery stays findable as work accumulates.
Transformation Workbench · the job
The blueprint for the job, and the factory that builds it
Integrate the acquisition, evidence the regulation, reset who owns the work, decide what you will not fund: the same class of job comes back every cycle. A transformation workbench is the blueprint for one of those jobs, and the factory is the engine that builds it. Describe the work your team repeats, and the workbench is the result.
A blueprint for one job
One workbench, one repeating job: the stages in order, the brief at each one, and the outputs they leave behind. Your team and the agent walk it stage by stage, and each gate is a person's read.
An engine, not a one-off
Describe the work your team repeats. The factory proposes a blueprint, writes it once you approve, and stops. The workbench is what you run, and one engine serves any job, process, or transformation work you bring it.
Status you can see
Status is the files that exist. Anyone can open a stage and see where the work stands, and what it left behind.
Close the cycle, keep the blueprint
When every stage has passed, close the cycle: the outputs move into the blueprint's archive and the stages clear for the next round. The workbench is the same one next cycle.
Governance
Governance lives in the loop, not a later tab
Dedicated harness, isolated by organization and conversation. Approval gates with previews of what will change. A visible timeline behind every artifact. In a market being asked to prove AI governance, the artifact doubles as the audit trail: which sources were called, what came back, what was accepted, and which model ran.
- Bring your own models, chosen per conversation. You keep the contract and the spend
- Isolated by organization, then by conversation. A dedicated harness keeps connections and models inside the boundary that owns them
- Reads-first posture: nothing writes back in the default flow
- Approval gates with dry-run previews; timeouts fail closed
- A visible run timeline behind every artifact — provenance and the audit of what ran
- Artifact rendering sandboxed away from your workspace
Bring one real question.
Connect a source, give the agent something real to work on, and watch it build the answer on the canvas with you.