AI agents run your operations. You set the rules.

Work that is high in volume, governed by clear rules and already measured, where most actions can be undone. Discovery ranks your candidates by value and by risk, and the first agent is the one with the best return at the lowest consequence. Higher-stakes work follows once the first agent has earned trust in production.

Against a baseline taken before the build, on numbers your business already reports: cost per case, time to resolution, error rate and the share of work closed without a person. The targets are agreed in writing during discovery, the agent is graded on your past cases before launch, and the same numbers are reported every month after it.

It depends on whether the path is fixed. A chatbot answers questions, and a workflow or RPA bot repeats a fixed path and breaks when the path changes. An AI agent decides and completes the task: it reads from your systems, acts through a tool, checks the result and logs what it did. When the path is fixed, a plain workflow is cheaper and safer, and we say so on the first call.

Only what your policy allows. Every action is classed as read, reversible or irreversible: reads and reversible changes run alone, logged and with a one-step undo, and anything irreversible, such as moving money, deploying to production or messaging customers, waits for a person you name. The policy is a file in your repository, reviewed and merged like any other change.

Your people stay accountable, and the trace shows them exactly what happened. Every step records the input, the tool called and the policy version in force, and every irreversible action records who approved it. A reversible mistake is undone in one step, and each incident becomes a case in the regression suite that every later change must pass.

No. The agent runs in your cloud account or your data center, models are called through enterprise endpoints configured for zero data retention or run as open weights inside your environment, and nothing is used for training. Prompts, traces and evaluation data stay in your storage, under your access controls.

It is built for one. Controls are documented against the NIST AI RMF, ISO/IEC 42001 and the EU AI Act, and every change is tested against the OWASP Top 10 for Agentic Applications, from prompt injection and goal hijack to tool misuse and memory poisoning. Your reviewers receive the architecture, the data flows and the policy file before any build is approved.

You switch when your own evaluations say it is better. Models are configuration, so a new one is a config change followed by a run of your graded cases, and it ships only if quality, cost and speed hold. Different steps can use different models, with the most capable one kept for the hardest decisions.

On a shared foundation built once: a registry of typed tools, one policy engine, one evaluation suite, one approval queue and one trace store. Every new agent inherits those controls from its first day, so each one ships faster than the last and your risk team reviews the platform once.

Yes. Agents act through APIs and MCP servers we build and document for SAP, Salesforce, ServiceNow and your own services. Where a system has no usable API, such as a legacy desktop, a vendor portal or a terminal screen, a computer-use agent operates it through the screen in an isolated, recorded session. If a system cannot be automated safely, we say so during discovery.

Yes. The Agent2Agent protocol (A2A) lets agents hand tasks to each other across companies and vendors, and the Model Context Protocol (MCP) lets them share tools and data. Each agent keeps its own identity, permissions and audit trail, so a partner's agent can ask and your policy decides.

Four things drive the cost: how many systems the agent must act in, how usable their APIs are, how many decisions it may take alone, and how strict your audit and review requirements are. Discovery ends in a written proposal for each phase, approved before it starts. Running cost is model inference plus hosting in your environment, modeled per task during the build and held by spend caps in production.

Discovery takes two weeks and ends in a dated plan for your case, set mostly by how many systems the agent has to act in. From then on you see working software every two weeks, and the agent runs in shadow mode beside your team before it is allowed to act alone.

All of it: the code, prompts, tools, policy file, evaluation suite and runbook, in your repositories and your environment from the first commit. Your team can run, change and extend the agents without us, and we stay on to run them only if you choose.