Autonomy without surrendering control
Give teams a professional workspace of their own, with prepared agent roles, shared business knowledge, and permissions your organization controls. Solo turns capable agents into governed operations—separate from employees’ personal AI accounts. Talk with us about cloud and private deployment options.
The architectural question
Where does control live?
The obvious way to deploy an agent is to hand a powerful model direct access — a browser, a terminal, your systems — and let it run. The capability is real. But in that architecture, everything that matters lives inside the model's judgment: what it should do, what it may touch, when to stop, when to ask. The more capable the model, the more your organization is trusting the intelligence itself to be the safeguard.
Solo's answer isn't less autonomy. It's governed autonomy: governance abstracted out of the model and into its own orchestration layer — one that always knows what work is happening, who is acting, what they're allowed to do, and when a person must weigh in. Reasoning is probabilistic. Execution isn't.
That layer is built from your business itself: the identity and its knowledge base, the persona and tools of each agent, and the escalation paths that bring the right person in on response times you define. The platform checks authority independently of the model's response. A persuasive prompt does not grant a new permission or change which business an agent serves.
Applied AI
An operating system, not another chatbot
Solo orchestration is not ChatGPT, Claude Code, or a general-purpose assistant with your logo on it. It's an operating system built around an identity — a business, a consultant, an employee acting as the human in the loop for an agent. Your intelligence and your workflows live in the operating system; models are the interchangeable engine underneath.
We call it Applied AI: business intelligence and workflows, abstracted into orchestration, and applied with the right model for the right job.
That separation also gives you control over inference costs. Choose models suited to each agent's work, from routine responses to complex judgment. As models improve, update that choice while keeping the business knowledge, permissions, and workflows your team has built.
How it's built
Five layers, one operating model
Control isn't a feature bolted onto agents. It's the structure the agents work inside.
Your organization sets the boundaries
Which identity an agent acts as, which tools it may use, and what requires sign-off are decided by your organization and held by the platform — not left to a model's judgment in the moment.
Agents reason and work
Inside those boundaries, agents operate with real autonomy — reading, drafting, scheduling, researching, following through — using the models and capabilities configured for their work.
Solo governs execution
Every consequential action passes through the governance layer: one identity, allowed tools only, approval gates where you've placed them. Agents propose; the system disposes.
Everything is observable
Work is durable and stateful, and every state change appends to a permanent timeline — who acted, what happened, what it produced. Nothing is fire-and-forget.
People handle exceptions
Judgment calls escalate to the right person with full context, on response times you define. Everything else keeps moving. Your team supervises outcomes, not keystrokes.
In practice
What the governance layer actually does
Identity boundaries
Every agent, integration, and piece of work belongs to exactly one business identity. Access is scoped to that identity and checked by the platform before work executes.
Approval gates & escalation
Work can require sign-off before it starts, pause for guidance mid-flight, and put structured questions to its owner. Escalation paths are tuned per agent — who gets pulled in, and how fast.
A governed work lifecycle
Work moves through a real state machine — created, approved, running, waiting, done — with retries, dependencies, and duplicate-action safeguards built in.
A permanent record
Every job keeps an append-only timeline of what happened: each decision, each handoff, each result. Readable by a person, not just a log parser.
Credentials stay home
Agents never hold your keys. Integration credentials are encrypted at rest, and agents act through named, allowlisted operations — never an open proxy into your systems.
Output lands within bounds
Structured contracts define exactly what each agent can contribute and where it may write. An agent's reasoning is free; its reach is not.
Model independence
Use the best model. Keep your rules.
In Solo's architecture, model providers are suppliers of intelligence — not owners of your operation. Each agent is configured with the model that fits its job and its own spend budget, so inference costs are optimized continuously as the frontier moves. No lock-in, no migration project when a better or cheaper model ships — the orchestration layer, where your identities, approvals, workflows, and records live, doesn't change when the model does.
Models will keep changing. Your operating model shouldn't have to.
Work that runs itself — because the boundaries don't.
Bring governed autonomy to your organization
Solo deploys on your existing infrastructure for complete control, with custom implementation from our team. Tell us about your operation and we'll show you what a governed agent team looks like inside it.