AI agents
An agent reads the request, uses the tools you allow, checks its own result and acts: answers the customer, updates the record, routes the exception. Outproduce builds agents for one job at a time, with scoped permissions, approval gates and a log of every step.
One direct Fit Review. No sales handoff.
A general assistant can draft an answer. It cannot safely run company work until its sources, tools, action rights, failure behaviour and owner are explicit.
One run
Every step is recorded with its input, the decision, and whether it ran on its own, waited for a person, or was read back from the receiving system.
Customer asks where order 4821 is
Shipped on the second, carrier reference found
In transit, delivery window Friday
Confidence above threshold. Below it, the case goes to a person with the context
Reply sent, ticket closed, every step logged
Each step is written to the audit trail with the tool, the input and the decision.
The difference is tools and judgment. Our agents call your systems, follow your rules and know when to stop and ask a person. Autonomy is earned one action class at a time, never granted by default.
What we build
Pick the stage that matches where the work gets stuck. Each card names the manual state, the installed state, and the outcome the team can check.
Looks up the order, the account and the policy, then answers or resolves the ticket. Anything unclear or sensitive goes to your team with the context attached.
Answers how-to and data questions from your internal systems and documents, with sources attached, and routes what it cannot answer to the right owner.
Handles the judgment steps in admin work: is this invoice complete, does this claim match the policy, which team owns this request.
Scores every inbound enquiry against your criteria, enriches it from approved sources, and alerts the right person the moment a strong one arrives.
Researches accounts, suppliers or competitors across the web and your CRM, and writes a sourced brief in your format for a person to review.
When one job spans research, drafting, checking and filing, we split it across coordinated agents, each with its own tools and limits, and a person signs off at the end.
How it is put together
Each example begins with a trigger, crosses named records, assigns approval, and ends in a state people can verify.
The install connects to the CRM, inbox, project tool, finance system and documents that carry the work today. Every connection proves a read, a scoped write and a readback before it goes live.
See how a connection is provenMarks identify the software these systems connect to. No endorsement or partnership implied.The chosen model and framework do not define ownership. The agreement does. Code, prompts, credentials, context, data, deployment and ongoing evaluation are assigned explicitly.
| Control | What it means | Example |
|---|---|---|
| Scoped permissions | Each agent gets its own account with access to only the tools and records its job needs | Read orders, not payroll |
| Approval gates | You choose which actions wait for a person every time | Refunds, customer emails, deletions |
| Confidence threshold | Below the threshold the agent hands the case to a person instead of guessing | Two possible accounts matched |
| Full audit log | Every tool call, input and decision is logged so you can see why it did what it did | Who approved the refund, and when |
Rollout
Nothing acts on real records until it has been right under review. Authority is granted per action class and stays visible.
A single, frequent task with a clear definition of done and a metric agreed.
Success metric agreedConnected to your systems with the narrowest permissions that work.
Read-only firstThe agent drafts, a person approves. Accuracy measured on real cases.
Human approves every actionApproval gates stay on risky actions. Misses reviewed on a schedule.
Monitored in productionAfter launch
Nothing acts on its own until it has been right on your real cases with a person checking. Misses are traced in the log and fixed in the rules.
Back to the start: the decision becomes a rule or a new case.
Relevant proof
Outproduce-owned private buildPrivate systems show source intake, approved context, model runs, quality gates, human review, cost state and delivery routing.
A chatbot answers questions. An agent completes tasks: it reads the request, decides what to do, calls your tools, checks the result and acts. Outproduce builds agents that work inside your existing systems within limits you set.
Only what you give it. Each agent runs with its own scoped credentials, read-only to start. Write access is added one action class at a time, and risky actions can require a person every time.
Low-confidence cases go to a person before anything happens. When a miss gets through review, it is in the audit log. We trace the cause and fix the prompt, tool or rule in the next tuning cycle.
The model is chosen per step: a capable model for judgment-heavy steps, smaller models where speed and cost matter more. Your data is not used to train models. The model does not define ownership; the agreement does.
It depends on the tools it connects to, the volume it handles and how much review it needs. The running-cost page explains what drives spend. A fixed build price comes from the Operating Map.
Yes. You can own the agent and run it yourselves after a documented handover, or Outproduce operates it under a managed agreement with monitoring, tuning and a regular report.
Start here
Tell us what starts it, who owns the result, which tools carry it, and where it stalls. We will tell you whether it is ready to map.
The workflow repeats
One person owns the result
The records exist
Several tools carry the work