AI agents that complete work, not just answer questions
An AI agent is software given a goal, a set of tools, and permission to take several steps on its own — read the inbox, look the customer up in the CRM, check the calendar, draft the reply, flag anything it is unsure about. The difference from a chatbot is that an agent does work in your systems rather than only talking. The difference from an automation is that an agent chooses its own next step instead of following a fixed path.
For businesses in the United States and Canada · Written by Bob Gabule, Founder · Updated
The problem
The tasks that eat the most coordination time are the ones with branches: this enquiry needs a quote, that one needs a site visit, this one is a supplier, that one is a complaint that should reach the owner today. Fixed automations cannot handle branching judgement, so a person stays in the loop as a router.
What you get
- An agent scoped to one job with an explicit list of tools it may use, and nothing beyond that list
- Hard limits: what it may read, what it may write, what it must escalate, how many steps it may take before it stops
- A full trace of every run — what it saw, what it decided, what it did — so a wrong outcome can be diagnosed instead of guessed at
- Escalation paths to a named human, with the context attached, so handoffs do not lose information
- Evaluation against real historical cases before it touches live work
How it works
Define the job and the boundary
We write down exactly what the agent is responsible for, what it is forbidden to do, and what it must escalate. Agents fail from vague scope far more often than from weak models.
Test against your own history
We replay real past cases through the agent and compare its decisions to what your team actually did. That gives a measured accuracy figure before anything goes live.
Deploy narrow, widen slowly
It starts on one category of work with a human reviewing every action, then earns wider permission as the trace record justifies it.
A good fit if
- Multi-step processes with branches that a person currently routes
- Businesses with enough historical examples to test against
- Owners comfortable starting with a supervised agent rather than a fully autonomous one
Not a fit if
- Anyone wanting an agent to replace a role outright — that is not what the technology reliably does today, and we will not sell it
- Irreversible actions with no human check: payments, contracts, medical or legal advice
- Processes with no historical record, since there is nothing to evaluate against
Questions we get asked
What is the difference between an AI agent and a chatbot?
A chatbot converses. An agent acts: it uses tools — your CRM, calendar, database, email — to complete a task across several steps and then reports what it did. A chatbot that can only produce text is answering questions; an agent that can only produce text has not done its job.
How do you stop an agent doing something stupid or expensive?
Four mechanisms, all boring and all necessary: an explicit allow-list of tools, read-only access wherever writing is not required, a step budget that halts the run, and mandatory escalation categories that always reach a human. Every run is logged with its full reasoning trace, so when something goes wrong you can see exactly where.
How do we know it works before trusting it?
We replay your real historical cases through it and compare against what your team decided at the time. That produces an accuracy number on your own data rather than a vendor benchmark. If the number is not good enough, the honest answer is a narrower agent or a plain automation instead.
Which AI models do you use?
Whichever fits the task and the budget, and we keep that choice swappable rather than hard-wired. Cheap fast models handle classification and routing; stronger models handle drafting and multi-step reasoning. Model quality moves month to month, so anything built to depend on one specific model ages badly.
Often built alongside
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