AI Agent Development
Deploy supervised AI agents that do real work and stay inside your rules.
An agent is not a chatbot. It is a worker with a defined role, a defined toolbelt, and defined boundaries. We build agents that read your systems, draft the next action, and wait for a human approval before anything consequential happens.
Bounded roles. Tool access limits. Approval gates. Audit trails on every action. The opposite of autonomous.
First agent in production in 4 to 8 weeks.
Book an AI Agent Audit See How We WorkBounded role
Each agent has one job, one set of tools, and one set of permitted actions. No drift. No surprises.
Approval gates
Anything that touches money, customers, or production data waits for a one-click human approval.
Full audit trail
Every input, every tool call, every decision is logged with the source and the reason. Replayable. Reviewable.
Best fit
AI agents work best when there is a repeatable role with clear tools, clear inputs, and clear acceptance criteria for the output.
Start here when
- You have a role on your team that does the same kind of work over and over: triage, research, drafting, follow-up, reconciliation.
- You can describe what “good” looks like and what data the role needs to do its job.
- You want AI inside the workflow, but you also need controls, approvals, and an audit trail before it goes anywhere near production.
- You are tired of chatbots that answer questions and never close a loop.
This is probably not the first step when
- You want a fully autonomous agent making consequential decisions without approvals. We do not build that.
- The work has no clear inputs, no clear acceptance criteria, and no obvious owner.
- You want a generic AI assistant for the company. That is a different problem and rarely produces measurable ROI.
Problems we solve
Most teams that ask us about agents are recovering from the same set of failed bets.
Chatbot fatigue
You shipped a chatbot. It answers questions, sometimes. It never opens a ticket, updates a record, or closes a loop. The team stopped using it.
What we install: agents that take action on your systems and route the result for approval, instead of dispensing text into a chat window.
Prompt-only AI
Individual contributors copy-paste between ChatGPT and your tools. The output is inconsistent and never recorded.
What we install: a structured agent with the right context, the right tools, and outputs that flow into your systems with provenance.
Hallucinations on real workflows
An LLM made up a customer detail, a SKU, or a policy. Your team no longer trusts the output for anything that matters.
What we install: agents grounded in your sources, with structured outputs, confidence scoring, and forced citation of the data they used.
No audit trail
When something goes wrong, nobody can answer what the AI did, why, or with what data. Compliance and leadership both flag it.
What we install: a full audit log of every input, tool call, decision, approval, and override, with replayability for review.
What you get
Each agent ships with the controls, console, and observability the role actually needs.
Agent specification
The role, the inputs, the toolbelt, the permitted actions, the approval thresholds, and the success criteria.
Grounded prompt and tools
The prompt template, the retrieval sources, and the tool functions the agent can call, each with input and output contracts.
Approval console
The interface where a human reviews the agent’s draft, edits it, approves it, rejects it, or escalates it.
Observability and audit
Per-run logs, replay, accuracy tracking against ground truth, and clear failure modes for when the agent should not act.
This is not just…
Another chatbot, another GPT wrapper, or an autonomous agent with the keys to your business.
- Not just a chat interface that returns text and forgets the conversation.
- Not just a prompt that produces output nobody routes anywhere.
- Not just an autonomous agent that touches production systems without approvals.
- Not just a demo that wins a meeting and never makes it into the workflow.
It is a supervised worker with a defined role, defined tools, defined boundaries, and a human-in-the-loop on every consequential step.
What this looks like in practice
Every supervised agent runs through the same five-stage loop.
Trigger
An event, ticket, document, or scheduled run hands the agent its task.
Ground
The agent retrieves the data it needs from approved sources with citations.
Reason and act
The agent selects from a permitted toolbelt and produces a structured draft action.
Human approval
A reviewer approves, edits, rejects, or escalates with one click.
Audit and learn
The decision lands in the system of record. Every step is logged for replay and tuning.
Sample deliverable
Agent Role Specification
Before any code is written, you receive an agent role specification you can review with your team and your legal or security partners.
- The job-to-be-done and the inputs the agent expects.
- The permitted toolbelt and the systems the agent can read or write.
- The approval thresholds, escalation paths, and explicit “do not do this” list.
- The success metric and the false-positive and false-negative budget.
Where it shows up first
Inbound triage agent for a high-volume support queue
A growing operations team was reading every inbound ticket before any work began. Most tickets were obvious, but each one took a human read to classify, enrich, and route.
We built a supervised triage agent that read the ticket, retrieved the customer record, classified the issue, drafted a recommended next action, and presented the package to a reviewer who approved or edited it in seconds.
- The agent never closed a ticket on its own. Every action waited for a one-click approval.
- Confidence scoring routed obvious tickets to fast review and ambiguous ones to a senior reviewer.
- Every run was logged with inputs, retrieval sources, tool calls, and the approving reviewer.
How it works
Three steps, every engagement, every size.
Audit
We pick the role, define the inputs and toolbelt, and write the success metric.
- Role and acceptance criteria
- Tool and data inventory
- Approval thresholds and escalation
Design
We design the grounded prompt, the tool contracts, the approval console, and the audit log.
- Retrieval and grounding plan
- Tool function contracts
- Approval console and console UX
Implement
We build, evaluate against historical examples, deploy behind approvals, and tune.
- Production agent and console
- Observability and replay
- 30 days of post-launch tuning
Frequently Asked Questions
How is this different from a chatbot?
A chatbot answers questions in a chat window. An agent does the work. The agent reads from your systems, drafts a structured action, requests approval, and writes the result back to the system of record. The output is not text. The output is a record updated, a ticket routed, a follow-up sent, or an exception escalated, with a full audit trail.
What guardrails do you put on agents?
Bounded roles, defined tool access, approval gates on consequential actions, retrieval grounded in approved sources, structured outputs with required fields, confidence thresholds for review routing, and an explicit “do not do this” list per agent. Every step is logged and replayable. Nothing acts on production data without an approval recorded against a real human reviewer.
How do you handle hallucinations?
Three layers. First, retrieval grounds the agent in your approved sources, not the model’s training data. Second, structured outputs force the agent to cite the source for each field. Third, low-confidence outputs route to a senior reviewer instead of the standard reviewer. When the agent cannot ground a claim, it escalates instead of guessing.
Can agents touch our production systems?
Yes, under approval. Each tool the agent can call has a defined input and output contract, a permission scope, and an approval rule. Read-only tools may run without an approval. Tools that write to production systems wait for a one-click human approval, with the proposed action and source data shown for review. Approvals are logged. Overrides are logged. Failures route to escalation.
What models do you use?
Whichever model fits the role and the constraints. We use frontier models for reasoning-heavy roles and smaller, faster models for classification and routing. Where data residency or cost matters, we use private deployments. The architecture is model-agnostic, so you are not locked into one vendor.
How do you measure whether an agent is working?
Success metrics are written in the role specification before code is written. Common metrics include throughput, accuracy against ground truth, approval rate without edits, escalation rate, and time saved per task. Every metric is computed from the audit log so the numbers are auditable. We also track failure modes so you can see what the agent should not be asked to do.
What is the first step?
An AI agent audit. We pick one role with the highest leverage, write the role specification, define the toolbelt and approval rules, and produce a fixed-scope build plan with a defined success metric. You will know exactly what we would build, why, what it should cost, and how the result will be measured before committing to implementation.
Related services
Agents usually live inside a larger automation layer. If you are thinking bigger:
- AI Business Operating Systems: connect agents, workflows, tools, data, and reporting into one governed layer.
- AI Workflow Automation: when the work is more about routing and approvals than autonomous reasoning.
Build one agent. Govern it. Then expand.
You do not need a fleet of agents to get value. The faster path is one supervised agent doing one role well, observable and auditable, that produces a measurable result inside 8 weeks.
Wondering what AI agents look like in your industry? Our free reference site walks through use cases for 98 verticals: aerospace, funeral services, elevator services, dermatology, or browse the full index. Each industry hub has agent recipes, vendor comparisons, and ROI math.