Agents you can actually let act

Building systems that decide their own next step. An agent takes a goal, breaks it into tasks, calls tools and APIs to carry them out, checks its own results and adapts when something fails. That is different from a chatbot, which answers, and from a script, which follows a fixed path. The engineering work is mostly in the boundaries: what the agent may touch, when it must stop and ask, and how you undo what it did.

RPA follows rules you wrote and breaks when the screen or the process changes. Agents handle variation, unstructured inputs and judgement calls, which is where rule-based automation has always stalled. The trade is predictability: an agent needs evaluation, tracing and permission design that RPA never required. Where a process is stable and fully specified, RPA is still the cheaper answer and we will say so.

A single agent on a contained workflow reaches supervised pilot in six to eight weeks. Production rollout with full tracing and permissions adds four to six more. Multi-agent systems spanning several business systems run four to six months, largely because of integration and access approvals rather than the agent logic. We usually start with one workflow rather than a fleet.

You own the codebase, the tool definitions, the traces and the infrastructure. Agent logic is built behind an orchestration abstraction so the framework underneath can be replaced without rewriting the workflow, and tool access is exposed through MCP servers or standard APIs that any other system can call. We document what would need retesting on a switch.

Usually yes, and the integration layer is where most of the effort goes. We map what each system already exposes, build connectors or MCP servers for what it does not, and put a permission boundary in front of everything the agent can reach. Legacy systems without APIs can often be reached through database views or a service wrapper rather than screen scraping.

It earns it. Every agent starts in a supervised pilot where a human approves each consequential action and every run is traced. We measure task success, intervention rate, cost per run and time to recover from failure against a bar agreed up front. Autonomy is widened action by action as the evidence supports it, and the rollback path stays in place either way.