AI Agents That Do The Work, Not Just Chat

An AI agent is software that plans and carries out multi-step work rather than only answering questions. It takes a goal, works out the steps, uses your tools and data to act, and finishes the task, inside boundaries and human oversight that you set. The distinction that matters commercially is between answering and doing. A system that tells your support team how to issue a refund is useful; a system that checks the order, applies the policy, issues the refund and writes the note is doing the job. An agent is the second kind, which is also why the limits around it deserve more care.

Yes, and that is usually the sensible shape rather than a compromise. Deterministic automation is cheaper, faster and more predictable for the steps that never vary, so there is no reason to hand those to a model. The agent earns its place on the parts that need judgement: reading something unstructured, deciding which path applies, handling a case the rules never anticipated. In practice we often leave your existing workflow tools doing exactly what they do well and give the agent the exceptions they currently dump into somebody's inbox. That also keeps the change small enough to evaluate, because only one part of the process has actually moved.

Roughly what a capable new starter would need, which is a more useful test than any technical checklist. It needs to reach the systems where the answers actually live, and it needs your rules written down somewhere rather than held as team knowledge, because an agent cannot absorb the convention that nobody ever documented. Gaps show up quickly and specifically, which many clients find is a side benefit: the questions an agent cannot resolve are usually the same ones that slow down onboarding. Where documentation is thin we start with the narrower slice that is well covered, instead of waiting for a documentation project to finish first.

You are, in the sense that matters commercially, which is exactly why the design has to make that reasonable to carry. Every action is logged with what was done, which tool was called, what data was read and what the agent was working toward, so an incident can be reconstructed rather than guessed at. We name a human owner for each agent during design, with the authority to pause it, and agree in advance which failures escalate immediately and which get picked up in a weekly review. An agent nobody owns and nobody reviews will eventually do something surprising, and the missing owner is what makes that a real problem.

It breaks, and the only question is whether you hear about it from monitoring or from a customer. Integrations are the part of an agent build needing ongoing attention, because the systems on the other end ship changes on their own schedule and retire versions without asking. We pin API versions where the vendor supports it, monitor error rates and response shapes rather than only uptime, and keep tool definitions in one place so a change is a single edit instead of a hunt through the codebase. Known deprecation dates go into your maintenance plan at handover, since those are the failures you can actually see coming.

Multi-step work that needs both judgment and action: resolving support requests, qualifying and updating leads, processing orders, preparing quotes, coordinating across systems and handling routine operations. A useful test is whether a person currently looks several things up and then does something with what they found, because that shape of work is where agents earn their keep. The tasks that suit them least are the ones with a single correct answer and no lookups, which a simple rule handles more cheaply and more predictably, and we will say so rather than building something more interesting than the problem requires.

Yes. For larger processes we build several specialized agents that coordinate, each owning part of the job and passing work along, with oversight across the whole flow. Worth being honest about when this is warranted. Multiple agents add coordination and make failures harder to trace, because the question stops being what went wrong and becomes which one went wrong and what it passed on. So we reach for it when the work genuinely divides into parts with different tools and different judgment, rather than as a default architecture. A single well-scoped agent is easier to trust and considerably easier to debug.

Testing against real scenarios before launch, including the awkward ones, since agents fail on ambiguity rather than on the happy path everybody demonstrates. Then guardrails and human review on the steps that matter, and monitoring of decisions and outcomes in production, because behavior drifts as your data and processes change even when nothing about the agent changed. The approach we favor is starting with a narrow remit the agent handles well and widening it as evidence accumulates. Reliability here is demonstrated over time rather than certified at launch, and any claim to the contrary should be treated with suspicion.

Usually it removes repetitive coordination and lookup work rather than people. Agents absorb routine volume so your team spends its time on exceptions, judgment and customers, with humans still owning the decisions that matter. There is a practical reason this tends to be true rather than merely reassuring: the work that automates cleanly is the work that is already well defined, and most roles are a mixture of that and work requiring context, negotiation or accountability. What usually changes is the shape of the job, with more time on the difficult cases, since the easy ones stop reaching a person at all.

Yes, and with agents the question is broader than storage, because an agent also acts. Your data stays under your control, is handled securely, and is never used to train models for anyone else. Where it is sensitive we can run private or self-hosted models so nothing leaves your environment at all. The agent-specific part is reach: it holds credentials to real systems, so those are scoped to the task rather than to whatever an existing account happened to permit, and every action it takes is logged. That log is what lets you answer afterwards exactly what was touched and when.

A focused single-task agent is usually a few weeks. A multi-agent system with several integrations takes longer, and what stretches it is rarely the model work. It is access: getting credentials, understanding an API that is documented loosely, and establishing what the process actually does as opposed to how it is described. We scope and estimate up front, and normally suggest starting with one well-defined agent to prove the approach on something real. That first build also surfaces the integration problems early, while the scope is small enough for them to be cheap to solve.

Describe the task or process you would like an agent to handle using the form on this page. We reply within one business day with an honest view of what is realistic and a scoped plan. The most useful thing you can include is the sequence a person follows today, including the steps where they check something before proceeding, because those checkpoints are where the autonomy boundary usually belongs. If what you describe would be better served by a rule, a report or a chatbot rather than an agent, we will tell you that instead of quoting for the agent.