AI Agents

Custom AI Agents That Complete a Task, Not Just Answer a Question

Most AI tools stop at giving an answer and leave the work to a person. An AI agent is built to carry the task through: it checks the data, calls the systems it needs, takes the steps in order, and stops to ask a person only where a decision needs approval. We build agents around a specific task in your business, connected to your own systems.

50+ ERP and software projects. Zero failed projects since 2021.

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Agent Run: New Order Check

Illustrative

Goal: verify stock and credit before releasing order #10482

Checked stock across 3 warehouses

Read from ERP inventory module

Checked customer credit position

Read from accounts receivable

Found balance 4% over limit

Flagged, not overridden

Prepared release request

Sent to credit controller

✅ Waiting on human approval before release

Figures are illustrative

Task

Completed, not just answered

Your

Systems, connected directly

4-8 wk

To a first working agent

Human

Approval on the steps that matter

Why It Matters

A chatbot answers. An agent gets the work done

Most of the value in daily operations sits in the multi-step tasks nobody has time to automate one click at a time.

Built Around One Task

Start narrow, prove it, then extend

A general-purpose agent that tries to do everything is hard to trust and hard to test. We build around one clearly defined task first, such as checking an order before release, so it can be tested against real cases before it touches anything live.

One task defined precisely before any building starts.
Tested against real historical cases first.
Extended to nearby tasks once it is proven.

A business process being automated step by step

Approval Where It Matters

The agent works, the person decides

Every step the agent takes is classified as safe to run alone or needing sign-off. A stock check can run unattended. Releasing an order over a customer's credit limit cannot. That line is agreed with you before anything is built, not discovered after something goes wrong.

Each step classified as automatic or needs approval.
A full record of every action the agent took.
Approval limits set by your policy, not ours.

A manager reviewing and approving an automated task

An Agent in Action

What a task looks like from the inside

Every step the agent takes, in order, with the record it read and the decision it left for a person.

Agent Run: Supplier Follow-Up

Illustrative

Goal: chase 3 overdue purchase orders and update the buyer

Identified overdue purchase orders

Compared promised date against today

Drafted a follow-up email per supplier

Referenced PO number and quantity

Sent 2, held 1 for review

Held email flagged a price mismatch

Logged the outcome

Summary posted to buyer's task list

✅ 1 case held for the buyer to check before sending

Figures are illustrative

How It Works

From a task on paper to an agent your team trusts

Define the task

We write down the goal, the steps, the systems involved and where a person must approve.

Connect the tools

We build the connections the agent needs into your ERP, email or other systems.

Build the agent

We build the planning and action logic, and the rules for what needs sign-off.

Test on real cases

The agent runs against real historical cases before it is allowed near live data.

Run and monitor

The agent runs in production with a full log, and we extend it once it is trusted.

What It Handles

The kind of task worth building an agent for

Multi-step, repetitive, and clearly defined enough to test properly before it runs on its own.

📋

Data checks before an action

Stock, credit or compliance checks that must pass before an order, invoice or shipment proceeds.

✉

Follow-ups and reminders

Chasing overdue payments, pending approvals or missing documents across multiple contacts.

🔍

Research and gathering

Pulling together information from several systems into one summary for a person to act on.

📋

Document preparation

Drafting a quote, a report or a form from source data, ready for a person to review and send.

🔄

Cross-system updates

Keeping a record consistent across two or more systems that do not talk to each other today.

⚠

Exception handling

Spotting the cases that break the normal pattern and routing them to the right person with context.

What Changes

What a working agent changes

Fewer

Manual steps per task

Faster

Turnaround on routine work

Logged

Record of every action taken

Consistent

Handling of every case

Safe

Approval kept on risky steps

Reusable

Pattern for the next task

Why Us

Why teams choose TCB Infotech to build AI agents

We know the systems underneath

We build ERP systems, so we connect agents to real business data correctly, not through guesswork.

Narrow scope first

We start with one task done well rather than a broad agent that is hard to trust or debug.

Approval built in from day one

Risky steps stop for a person by design, not as an afterthought once something goes wrong.

Full action logs

Every step the agent takes is recorded, so its behaviour can always be reviewed and explained.

Model-agnostic build

We use the approach that fits your data and hosting rules, with no lock-in to one vendor.

Proven delivery

50+ projects across industries, with zero failed projects since 2021.

FAQ

AI agent development questions

What is an AI agent?

An AI agent is a program that is given a goal, works out the steps needed to reach it, and carries out those steps by calling the tools and systems it has been connected to, such as your ERP, email or a database. It reports back with what it did and what it found, rather than just answering a single question.

How is an agent different from a chatbot?

A chatbot answers questions in a conversation. An agent is built to complete a task: it can check a record, call an API, update a system and move to the next step on its own, then stop and ask a person when a decision needs approval.

Will the agent act on its own without approval?

That depends on the task and is agreed with you upfront. Low-risk, reversible steps can run automatically. Anything with financial, legal or customer impact is built to stop and wait for a person to approve before it goes further.

What systems can an agent connect to?

Anything with an API or a supported connector: your ERP, CRM, accounting system, email, spreadsheets and internal tools. If a system has no API, we build a narrow integration for the specific steps the agent needs.

Which AI model does the agent use?

We build model-agnostic, choosing the approach that fits your data, budget and hosting requirements rather than committing you to one vendor. The agent's logic and tool connections stay the same if the underlying model is changed later.

How long does a first agent take to build?

A first working agent for one well-defined task usually takes four to eight weeks, depending on how many systems it needs to connect to and how much testing the task requires before it runs unattended.

In Short

The value is in the task finished, not the answer given

An agent is worth building when the task is repetitive, spans more than one system, and currently takes a person real time to do end to end. If the task is a single lookup, a chatbot already covers it.

Start with one task, prove it against real cases, keep a person in charge of the decisions that carry risk, and extend from there. That is how an agent earns trust instead of demanding it upfront.

Find the Task Worth Automating With an Agent

Book a free consultation. Describe the task your team repeats every week, and we will show you what an agent for it would look like.

Book a Free Consultation →

No commitment. A practical look at where an agent would pay off first.