AI Agents
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
Why It Matters
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.
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.
Given a goal, the agent works out the sequence of checks and actions needed, adjusting the plan as it learns what a step returned. This is different from a fixed script, because the agent can handle the variation that real business cases always contain.
The agent calls your ERP, CRM, email or internal tools through defined connections, each scoped to exactly what the task needs. It does not get broader access than the job requires, and each connection is tested against your live data structure before the agent runs unattended.
Steps are classified during design as automatic or requiring sign-off, and that classification is enforced in the running agent, not left to chance. A complete log of what the agent did, when, and why is kept for every run, so behaviour can be reviewed at any time.
Stock, credit or compliance checks that must pass before an order, invoice or shipment proceeds.
Chasing overdue payments, pending approvals or missing documents across multiple contacts.
Pulling together information from several systems into one summary for a person to act on.
Drafting a quote, a report or a form from source data, ready for a person to review and send.
Keeping a record consistent across two or more systems that do not talk to each other today.
Spotting the cases that break the normal pattern and routing them to the right person with context.
We write down the goal, the steps, the systems involved and where a person must approve.
We build the connections the agent needs into your ERP, email or other systems.
We build the planning and action logic, and the rules for what needs sign-off.
The agent runs against real historical cases before it is allowed near live data.
The agent runs in production with a full log, and we extend it once it is trusted.
Manual steps per task
Turnaround on routine work
Record of every action taken
Handling of every case
Approval kept on risky steps
Pattern for the next task
We build ERP systems, so we connect agents to real business data correctly, not through guesswork.
We start with one task done well rather than a broad agent that is hard to trust or debug.
Risky steps stop for a person by design, not as an afterthought once something goes wrong.
Every step the agent takes is recorded, so its behaviour can always be reviewed and explained.
We use the approach that fits your data and hosting rules, with no lock-in to one vendor.
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.
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.
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.
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.
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.
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.
Related
The other services in this section, and related pages from TCB Infotech.
No commitment. A practical look at where an agent would pay off first.
