Business agents for intake, triage, quoting, and follow-up
Single-purpose AI agents that handle one business workflow end to end, with tool access, validation, and a human approval step where the work requires it.
A business agent is a program that uses a language model to work through one specific task, calling your systems as it goes, rather than answering a single question. Intake, triage, quoting, and follow-up are the jobs where this pays off most. It is for teams where one workflow arrives constantly, takes several steps, and each step is judgment plus lookup.
The problem
A chatbot answers a question. Most business work is not a question, it is a sequence: read this, look that up, check the rules, decide, draft the response, log it. Handing that sequence to a person is expensive and handing it to a rigid workflow tool fails on the variation.
- Inbound requests arrive as free text and someone has to read each one to route it.
- Producing a quote means checking specs, pricing rules, stock, and past orders across three systems.
- Follow-up gets skipped when the week is busy, which is when it matters most.
- Rules-based routing was built once, and now nobody adjusts it because the logic is unreadable.
- The work is too varied for a form and too repetitive to justify a person doing it all day.
What we build
A scoped agent, not a general assistant
One workflow, one clear definition of done, and a bounded set of tools. Narrow agents are the ones that work reliably enough to trust.
Tool access to your systems
The agent reads and writes through defined functions against your CRM, database, pricing, and inbox, with permissions scoped to exactly what the job needs.
Deterministic business rules
Pricing, eligibility, discounts, and thresholds live in code the agent calls, not in a prompt. The model decides what to do, not what a number is.
A stopping condition
Explicit limits on steps, tool calls, and spend, so a confused agent stops and escalates instead of looping.
An approval step
Where the output goes to a customer or changes money, a person approves it. The agent does the assembly, the human keeps the judgment.
A full trace of every run
What came in, which tools were called with what arguments, what came back, and what the agent produced. Without this you cannot debug it or improve it.
What you get at handoff
- Agent code in your repository, calling your own model accounts.
- The evaluation set built from your real historical cases, plus the scoring script.
- A runbook covering the approval queue, escalation, and the manual fallback.
- Traces and cost dashboards you can read without us.
- Training for the people reviewing the output daily.
Stack
Models
Claude, OpenAI, Tool use and structured outputs
Language
Python, TypeScript
Business systems
HubSpot, Salesforce, Stripe, QuickBooks, Postgres, Email
Runtime
AWS Lambda, Cloud Run, Queues, Scheduled triggers
Operations
Run traces, Evaluation sets, Cost tracking, Approval queues
Related services
FAQ
How is an agent different from an automation?
An automation follows a path you defined. An agent decides the path within limits you set, which is what makes it useful when inputs vary and expensive when it is given too much freedom. If the steps are always the same, you want an automation, and we will say so.
What stops it from doing something wrong?
Three things. Tool permissions scoped so the destructive actions are not available. Business rules in code rather than in the prompt. An approval step before anything reaches a customer or changes money. The model gets to be wrong about wording, not about pricing.
How many agents does a small business need?
Usually one, at first. One workflow, shipped and measured, teaches you more about whether this fits your business than a platform-wide plan. Multi-agent designs are a later problem and often an avoidable one.
What does it cost to run?
Cost scales with how much text each run processes and how many tool calls it makes. Scoping estimates this from your real volumes, and cost per run is tracked from day one so a change that doubles it is visible immediately.
What happens when the model gets confused?
It hits a step or spend limit and escalates to a person with the full trace attached. Silent failure is the outcome worth engineering against, so an agent that stops and asks is behaving correctly.
Ready to scope Business Agents?
Send the workflow, tool stack, or reporting problem. We will tell you what should be automated, what should stay manual, and what is worth building first.