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Agentic AI

AI agents that do the work, not just answer

We design, build and operate AI agents that plan a task, use your systems, check their own results and ask a person when it matters.

What is agentic AI?

Agentic AI is artificial intelligence that takes actions toward a goal instead of only answering. An AI agent plans the steps, calls tools and business systems, checks its own results and continues until the task is done, asking a person for approval when the stakes are high.

Agents we build

Seven kinds of agent, one for each job

Each one is built around your process and connected to the systems you already use.

Customer Support Agent

Answers customers from your own knowledge and resolves routine requests end to end.

  1. 1Customer message
  2. 2Looks up, answers, acts
  3. 3Resolved or escalated
  • Answers questions with sources
  • Checks orders, bookings and accounts
  • Hands over to a person with full context
  • Helpdesk
  • CRM
  • Order system
  • WhatsApp

Sales & Lead Agent

Qualifies inbound leads, researches them and keeps your CRM up to date.

  1. 1New lead
  2. 2Qualifies and researches
  3. 3Meeting booked, CRM updated
  • Replies to new leads in minutes
  • Researches the company and contact
  • Books meetings and drafts follow-ups
  • CRM
  • Email
  • Calendar
  • Website forms

Voice Receptionist Agent

Answers and makes phone calls, books appointments and takes messages.

  1. 1Incoming call
  2. 2Understands and books
  3. 3Appointment confirmed
  • Answers calls at any hour
  • Books, moves and cancels appointments
  • Transfers to staff when needed
  • Phone system
  • Calendar
  • CRM
  • SMS

Document & Back-Office Agent

Reads invoices, forms and contracts, enters the data and flags what needs review.

  1. 1Document arrives
  2. 2Reads, checks, enters
  3. 3Posted or sent for approval
  • Extracts data from PDFs and scans
  • Matches against your records
  • Queues exceptions for approval
  • Accounting
  • ERP
  • Email inbox
  • Cloud storage

Data Analyst Agent

Answers business questions from your data in plain language, with the query shown.

  1. 1Question in plain English
  2. 2Queries and analyses
  3. 3Answer with chart
  • Turns questions into queries
  • Builds charts and summaries
  • Sends scheduled reports
  • Data warehouse
  • Databases
  • BI tools
  • Slack / Teams

IT & DevOps Agent

Triages alerts and tickets, follows your runbooks and suggests or applies fixes.

  1. 1Alert or ticket
  2. 2Diagnoses with runbook
  3. 3Fix proposed
  • Reads alerts, logs and tickets
  • Runs diagnostic steps
  • Proposes a fix for engineer approval
  • Monitoring
  • Ticketing
  • Cloud console
  • Git

Multi-Agent System

Several specialised agents coordinated by a supervisor for longer, multi-step work.

  1. 1Complex request
  2. 2Agents plan, work, review
  3. 3Finished deliverable
  • Splits work across specialist agents
  • Supervisor checks each result
  • Runs long tasks that can pause and resume
  • Your internal APIs
  • Model Context Protocol
  • Queues
  • Databases

Inside an agent

What an AI agent is made of

Seven parts. Tap each one to see what it does and how we set it up for you.

PART 1 OF 7

Model

The language model that reads the task and decides the next step.

In your project: We pick it per step: a strong model for decisions, a cheaper one for routine work.

How much should AI do?

From chatbot to multi-agent: slide and see

The same refund request at four levels of autonomy. Orange is what the AI handles.

Does the task. You approve the risky step.

Same task at every level: a customer asks for a refund

  1. Read the requestAI
  2. Find the orderAI
  3. Decide by policyAI
  4. Issue the refundAI, you approve
  5. Reply and logAI
You AI

Chatbot vs agent

Why an agent, not another chatbot

ChatbotAI agent
What it does Answers questions Completes tasks
Uses your systems Read-only, if at all Reads and updates them
Steps One reply at a time Plans and runs many steps
Checks its work No Yes, before finishing
Human approval Not applicable Built in for risky actions

Try it

Run an agent. You approve the risky step.

Pick a task and press run. The agent plans, uses its tools and stops to ask you before anything that matters.

Incoming task

Customer: "My order arrived damaged, I want a refund."

A simulation of how an agent works. Real agents run on your systems and data.

  1. 1

    Find the order

    Order system
  2. 2

    Check the refund policy

    Knowledge base
  3. 3

    Issue the refund

    Paymentsneeds approval
  4. 4

    Reply to the customer

    Helpdesk
Design your own agent in three stepsPick the job, the systems and the rules. Get a blueprint you can send to us.Open the Agent Builder

By team

Pick a team. See what an agent does.

  1. Customer question
  2. AI answers from your docs
  3. Resolved, or handed to a person

How we build

From task to trusted agent

  1. 01Map the task
  2. 02Connect tools
  3. 03Add guardrails
  4. 04Evaluate on real cases
  5. 05Launch & monitor

Working with us

Start small, then scale

Scope and price are agreed in writing before any paid work begins.

  1. STEP 1

    Discovery call

    Free 60 minutes. We map one process and tell you honestly whether an agent fits.

  2. STEP 2

    Proof of concept

    One agent on one workflow with your real data. Scope and price fixed before we start.

  3. STEP 3

    Production agent

    Integrations, guardrails, evaluation and monitoring, rolled out to your team.

  4. STEP 4

    Managed agents

    We keep agents accurate and current as your business and the models change.

Estimate

How much time could an agent free up?

Move the sliders to match one task in your business.

Time-saving estimate

Hours freed per month

87

Equivalent cost per year

$31,320 USD

An estimate from the numbers you entered, not a promise. The real share an agent can handle is what a proof of concept measures.

Check it on your process

Industries

Agents for your industry

Models and frameworks we build agents with

  • OpenAI
  • Anthropic Claude
  • Google Gemini
  • LangGraph
  • OpenAI Agents SDK
  • Claude Agent SDK
  • Model Context Protocol
  • n8n
  • Python
  • Node.js
  • PostgreSQL
  • pgvector

FAQ

Agentic AI: common questions

What is agentic AI?

Agentic AI is artificial intelligence that takes actions toward a goal instead of only answering. An AI agent plans the steps, calls tools and business systems, checks its own results and continues until the task is done, asking a person for approval when the stakes are high.

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

A chatbot answers one question at a time. An AI agent completes tasks: it reads and updates your systems, runs several steps in sequence, verifies the outcome and escalates to a human when needed.

Which business tasks are best suited to AI agents?

Frequent, rule-heavy tasks that involve reading, deciding and updating systems: answering customers, qualifying leads, processing documents, scheduling, reporting and first-line IT support.

Are AI agents safe to connect to our systems?

Yes, when built with controls. We give each agent its own least-privilege credentials, validate every action against policy, require human approval for risky steps and log everything so it can be audited.

Which frameworks and models do you use to build agents?

We use models from OpenAI, Anthropic and Google and open-source models, with frameworks such as LangGraph, the OpenAI Agents SDK, the Claude Agent SDK and Model Context Protocol servers. We choose per project on quality, cost and privacy.

How do we start with an AI agent?

Start with one narrow workflow. Book a free 60-minute discovery call, then we build a proof of concept on your real data so you can judge the result before committing to a full rollout.

Which task should your first agent take over?

Book a free 60-minute consultation. You'll leave with a feature list, a rough estimate and a practical first step — no obligation.

Book a free consultation