Autonomous Systems
AI agents that do defined work in your systems and stop where a person decides.
Autonomous systems for enterprise operations and software are AI agents that carry out defined work inside the systems an organisation already runs, and stop at checkpoints where a person decides. Solonomous engineers them with written boundaries: what each agent may read and change, which tools it may call, where it must wait for approval, and how every action is traced and can be reversed.
Each agent works under its own identity, calls only the tools its task needs, waits for a person where the rules say so, and leaves a trace of every step.
Agents for supplier invoices and customer order questions
Autonomous Back Office
Invoices prepared for approval
Customer Operations Agents
Order questions answered
RPA
Legacy screens, no API
AI Agents & Agentic Automation
Tools called within limits
Name the work you want agents to take on, and the service pages that cover it come back, each with its checkpoints.
Before anything consequential, the agent shows what it proposes, the evidence it used and what happens next.
Pay this invoice to the new bank account once your finance team has confirmed the change by phone.
Payments, legal commitments, messages to outside parties and deletions wait for the accountable person to approve, edit or reject.
Prompts, tools and approval rules change together in a release, and each release runs the regression suite before an agent uses it.
Services
The build services behind this architecture: agent development, and agents organised by the job they do.
Every system we build answers the same questions in writing: what an agent may do, which parts it is made of, where a person decides and how it is judged.
Search treats “autonomous systems” as vehicles, drones and robots. Here the phrase means software: agents that read information, plan steps, use tools and carry a task through to a result that can be checked, inside an organisation's operations and its software.
The same parts appear in every system we build, sized to the task. Single agents for contained tasks; planner, worker and reviewer agents when a process needs delegation and checking. Rules that decide which actions an agent may take alone, which wait for a person, and which it may never take.
Agents run defined workflows inside the systems you already use and stop at defined checkpoints; a small team decides the exceptions.
Each agent acts as a named principal with the least privilege its task requires. Credentials are issued per agent and per environment, tools are isolated, and actions outside an agent's scope are refused by the platform rather than left to the model's judgement.
An agent that says it finished is not evidence that it did. We evaluate agents against the task's definition of done: whether the right records changed in the right way, whether the tool calls were correct, how often a task needed a person, and what each completed task cost. Trajectories are scored as well as end states, so a correct answer reached by an unsafe route still fails.
Most organisations start with one defined workflow in one function: an agent that prepares, checks and routes one kind of work, with a person deciding the exceptions. That is the individual autonomous AI solution our forward-deployed engineers deliver.
Human control
Checkpoints are placed where the consequences are. An agent may gather information, prepare a draft or reconcile records without waiting for sign-off; it waits for a person before it moves money, signs or sends something with legal effect, contacts an outside party, deletes data or changes a policy. The person sees what the agent proposes, the evidence it used and what will happen next, and approves, edits or rejects it.
Exceptions are routed rather than guessed at. When an input falls outside what the agent was built and evaluated for, or when its checks fail, the task goes to the accountable person with its context attached. Every agent can be paused, and every deployment has a way to stop all of its agents at once.
Agents earn wider scope one workflow at a time. Each one is written down, built inside your systems, judged on outcomes and handed to the people who decide its exceptions.
We write the workflow down with the people who own it: its inputs, the systems it touches, its checkpoints and its definition of done.
The agent gets its own identity and scoped permissions, the tools its task needs, approval rules and a trace of every step.
It is scored on whether the right records changed, with adversarial cases, then run beside your team with every action waiting for approval at first.
Runbooks, the regression suite, a named owner for every checkpoint and a control that stops all agents at once. Then the next workflow.
Tell us which workflow agents should take on, which systems it touches and who must approve what. We will engineer the system around those answers.
