AI agent development

AI agents that do the work, inside your systems

An agent reads the request, uses your tools, checks its own work and acts: answers the customer, updates the record, routes the exception. We build agents for one job at a time, with scoped permissions, approval gates and a log of every step.
Book a 30-min agent callSee what agents cost
support-agent · runIllustrative example
  1. TaskCustomer asks: “Where is order #4821?”
  2. Toollookup_order(#4821)Shipped 2 Oct, DHL
  3. Toolcheck_carrier(DHL JD014…)In transit, ETA Friday
  4. CheckConfidence0.94Below 0.80 → handed to a person with the context
  5. Actsend_reply(customer)Reply sent, ticket closed
4 steps logged to the audit trail

Agent, not chatbot

A chatbot tells your customer where to look. An agent looks it up, does the task and closes the loop.

The difference is tools and judgment. Our agents call your systems, follow your rules and know when to stop and ask a person.

What we build

One agent per job, wherever the work piles up

Start with one. Each agent ships with its own permissions, a confidence threshold and monitoring.
  1. CustomersQuestions, orders, ticketsCustomer support agent
  2. OperationsERP, back office, adminERP and operations assistantBack-office agent
  3. RevenueLeads, research, outreachLead qualification agentResearch agent
  4. Whole processSeveral agents, one jobMulti-agent workflows
Select an agent to jump to its card.

Customers

Customer support agent

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.

Before
Every ticket waits in a queue
After
Routine ones resolved, rest escalated

Routine tickets closed without a person

Operations

ERP and operations assistant

Answers how-to and data questions from your ERP and internal docs, and routes the requests it can't handle to the right owner.

90%lower ERP support costATI Lab client result

Before
Same questions to the same people
After
Answered from your docs, routed if not

Support load off the ops team

Operations

Back-office agent

Handles the judgment steps in admin work: is this invoice complete, does this claim match the policy, which team owns this request.

Before
A person checks every case
After
Agent decides, flags exceptions

Judgment calls made within your rules

Revenue

Lead qualification agent

Scores every inbound lead against your criteria, enriches it, and alerts sales the moment a hot one arrives.

Instanthot-lead follow-upATI Lab client result

Before
Leads sit unsorted for days
After
Scored and routed on arrival

No ready-to-buy lead left waiting

Revenue

Research agent

Researches accounts, competitors or suppliers across the web and your CRM, and writes a sourced brief in your format.

Before
Hours of tab-hopping per account
After
A sourced brief, ready to review

Briefs in minutes, with sources

Whole process

Multi-agent workflows

When one job spans research, drafting, checking and filing, we split it across coordinated agents, each with its own tools and limits.

Before
People pass the job along
After
Agents hand off, a person signs off

A whole process automated, not one step

How an agent works

Your tools, your knowledge, your rules

The agent only knows what you connect and only does what you allow. Everything it can't do safely goes to a person.

How an AI agent is put together. Tools (CRM, ERP, inbox, APIs), Knowledge (Docs, policies, past cases), Rules (Permissions, limits, thresholds) feed into AI agent (Plans, calls tools, checks itself), which updates Acts (Updates, replies, routes), Asks for approval (Risky actions), Escalates (Low confidence, to a person), Logs (Every step, for audit).

Illustrative

Guardrails

You decide what it may do on its own

Agents earn autonomy action by action. These controls ship with every agent we build.

  • Scoped permissions

    Each agent gets its own account with access to only the tools and records its job needs.

  • Approval gates

    Refunds, emails to customers, record deletions: you choose which actions wait for a person.

  • Confidence threshold

    Below the threshold, the agent hands the case to a person instead of guessing.

  • Full audit log

    Every tool call, input and decision is logged, so you can see exactly why it did what it did.

Audit log · one run

  1. 09:14:02Task receivedinboxOrder status question
  2. 09:14:03lookup_orderagentRead-only, ERP
  3. 09:14:04check_carrieragentCarrier API
  4. 09:14:05Confidence 0.94agentAbove threshold
  5. 09:14:05send_replyagentAllowed without approval
  6. 09:20:41issue_refundagentHeld for approval
  7. 09:31:10Refund approvedsupport leadReleased
Illustrative

Rollout

From one job to a trusted agent

Nothing acts on its own until it has been right on your real cases, with a person checking.

  1. 01: Pick one job

    A single, frequent task with a clear definition of done.

    Success metric agreed

  2. 02: Build with your tools

    Connected to your systems with the narrowest permissions that work.

    Read-only first

  3. 03: Shadow mode

    The agent drafts, a person approves. We measure accuracy on real cases.

    Human approves every action

  4. 04: Go live and tune

    Approval gates stay on risky actions. Misses are reviewed and fixed monthly.

    Monitored in production

Prefer us to run it? See the managed AI employee. Only need the steps automated, no judgment calls? See AI automation services.

FAQ

AI agent questions

What is the difference between an AI agent and a chatbot?

A chatbot answers questions. An agent completes tasks: it reads the request, decides what to do, calls your tools (look up an order, update a record, send a reply) and checks the result. ATI Lab builds agents that act inside your existing systems, within limits you set.

What can an agent access?

Only what you give it. Each agent runs with its own scoped credentials, usually read-only to start. Write access is added action by action, and risky actions can require a person's approval every time.

What happens when the agent gets it wrong?

Low-confidence cases go to a person before anything happens. When a mistake does get through review, it's in the audit log; we trace the cause and fix the prompt, tool or rule in the monthly tuning cycle.

Which AI models do you use?

We pick the model per task. We are a Claude partner and use Claude for most reasoning-heavy agents, and smaller models where speed and cost matter more. Your data is not used to train the models.

How much does an AI agent cost?

It depends on the number of tools it connects to, the volume it handles and how much review it needs. The AI agent cost page breaks down build and running costs; we give a fixed quote after scoping.

Can you run the agent for us?

Yes. You can own the agent and run it yourselves, or have us operate it as a managed AI employee with monitoring, tuning and a monthly report.

Next step

Name the job you want off your team's plate

We'll tell you whether it needs an agent or simple automation, what it should be allowed to do on its own, and what it costs to build and run.
See what AI agents costBook a 30-min agent call