AI Agent Development

A single agent owns the whole goal: it reasons, picks its tools and returns the result. A multi-agent system divides that work between specialists, each with a defined role and a fixed agreement about what they hand to one another, which is the team setup described elsewhere on this page. More agents buy flexibility, but every handover costs coordination and time, and makes it harder to trace where something went wrong. A common route is a single agent as the first delivery, with a team as the growth path once the process needs several unrelated tasks handled at once.

Start with a version that proposes actions and commits nothing. The agent walks through the real process, picks its tools and records at each step what it would do, while a colleague confirms the action itself. That shows you against real cases where the reasoning holds and where it breaks down, at no cost when it is wrong. Once the proposals are consistently correct across a run of cases, give the agent the right to act, beginning with the simplest category of work.

Through memory, context and a plan for failure. Memory is the record of which steps the agent has already taken and what they returned; without it the agent starts its thinking over at every step and loses the thread of the assignment. Context is current, searchable documentation of your products, processes and exceptions, because an agent that cannot look up the exception will fill in the gap itself. And since any step can fail, decide in advance what happens then: retry, take a different route, or stop the task and report the reason.

Judge the agent on the outcome of the process, not on how many requests it touches. Useful measures are the share of tasks it completes end to end without intervention, cycle time compared with the manual process, the share it correctly hands back, and the share of sampled results that turn out to be wrong. Record a baseline of the manual process before you build, or there is nothing to compare against afterward. Review the reasoning steps as well, because a right answer reached for the wrong reason will not repeat reliably.

Building for the favorable case. The demo runs on the standard request, while daily work consists largely of exceptions: incomplete data, a system that does not answer, a request that falls outside the process as written. Gather a set of real cases first, the messy ones in particular, and test the agent against them before it is allowed to act on its own. Skip that test and you tend to notice a failure only once the damage shows up further along, in a wrong booking or an integration that quietly stopped working.