What is an AI agent?
An AI agent is software that combines a large language model with tools and rules so it can work towards a goal: read information, decide on next steps and take actions such as updating a CRM, drafting a reply or creating a ticket. Unlike a simple chatbot, an agent can complete multi-step tasks across several systems.
Where do AI agents create value today?
The strongest early use cases are repetitive workflows with clear inputs and outcomes:
- Customer support triage, answers from your knowledge base and ticket routing
- Sales operations: qualifying leads, enriching CRM records and drafting follow-ups
- Finance operations: reading invoices, matching purchase orders and flagging exceptions
- HR and IT help desks: answering policy questions and handling routine requests
- Operations reporting: summarising data from several systems into daily briefings
What are the risks, and how do you manage them?
The main risks are incorrect answers, actions taken without proper authority, exposure of sensitive data and unpredictable costs. They are managed with grounding in approved data sources, strict permissions for each tool the agent can use, human approval for high-impact actions, logging of every step, and evaluation tests that run before each release.
Data protection rules still apply. Choose deployments where your data is not used to train public models, and design for regulations such as India's DPDP Act and the GDPR where relevant.
What are the building blocks of an AI agent?
Most business agents share the same parts. A language model interprets the request and decides what to do next. Tools let the agent act — calling an API, searching a database, sending an email or updating a record. A knowledge layer retrieves the right information from your documents and systems so answers are grounded in your own data rather than the model's general training.
Around these sit the parts that make an agent safe to run in a business: an orchestration layer that controls the sequence of steps, permissions that limit which tools and data each agent may use, approval steps for sensitive actions, and logging that records every input, decision and action for later review.
Should you build a custom agent or buy one?
Many business applications now include built-in AI assistants, and for common tasks inside a single product they are often the quickest option. A custom agent makes sense when the workflow spans several systems, relies on your own data and rules, needs specific approval steps, or is important enough to your operations that you want control over how it behaves and what it costs to run.
A useful test is to write down the workflow step by step. If every step happens inside one tool you already use, check what that vendor offers first. If the steps cross several tools, involve judgement based on your own policies, or need an audit trail, a custom agent is usually the better investment.
How do you measure whether an agent is working?
Agree the measures before the build starts and record a baseline from the current process. Review the numbers every week during the pilot, and expand the agent's responsibilities only when they stay stable.
- Task success rate: the share of tasks completed correctly without human correction
- Accuracy on a fixed evaluation set of real examples, re-run after every change
- Human override rate: how often reviewers reject or edit the agent's work
- Time saved per task and in total across the team
- Cost per task, including model usage and the time people spend reviewing
How do you prepare your team?
Agents change how people work, so involve the team that owns the workflow from the start. They know the exceptions and edge cases that documentation misses, and their trust determines whether the agent is used. Explain what the agent will and will not do, who reviews its work and how to report problems.
Give reviewers simple tools to approve, correct or reject the agent's output, and treat their corrections as training data for improving prompts, rules and evaluation tests. Over time, people spend less effort on routine steps and more on the judgement calls the agent sends to them.
Which workflows are a poor fit for AI agents?
Agents are not the right answer for every problem. Avoid starting with work that happens rarely, where each case is completely different, where a single mistake would be very costly, or where the underlying data is incomplete or unreliable. Fixing the process or the data first often delivers more value than adding AI on top.
Simple, rule-based tasks with no judgement involved are usually better handled by conventional automation, which is cheaper to run and fully predictable. Agents earn their place where the work involves reading unstructured information — emails, documents, conversations — and making routine decisions based on it.
What security questions should you ask?
Before an agent touches business data, agree the answers to a few questions with your IT or security team and your development partner.
- Which model provider is used, and is our data excluded from model training?
- Where is data processed and stored, and does that meet our data-residency obligations?
- Which systems can the agent access, with which permissions, and who approves changes?
- How are prompts, outputs and actions logged, and who can review them?
- How do we switch the agent off quickly if something goes wrong?
What does a realistic first project look like?
Consider a typical example: a company receives a few hundred supplier invoices by email every week, and a finance assistant types each one into the accounting system. A first agent reads each invoice, extracts the supplier, amounts, tax and due date, matches it to the purchase order and prepares the entry. A person reviews and approves each entry with one click, and anything that does not match is sent to them with the reason.
This kind of project has the qualities that make a good starting point: high volume, clear inputs, a measurable baseline and a natural human checkpoint. After a few weeks of reviewing results, the team can decide whether to let well-matched invoices through automatically and focus reviews on the exceptions.
What does it cost to run an agent?
Running costs come from model usage, which depends on how much text the agent reads and writes per task, plus hosting, monitoring and the time people spend reviewing its work. Choosing the smallest model that meets your accuracy target, sending only the information each step needs, and caching repeated lookups keep costs predictable. Track cost per task from the pilot onwards so there are no surprises at scale.
How do you launch your first AI agent?
A proven sequence:
- Pick one workflow and define success in numbers (time saved, accuracy, volume)
- Map the data sources and systems involved, and clean up access permissions
- Build a proof of value in a few weeks with a small group of real users
- Add guardrails, monitoring and human review, then compare results with the baseline
- Roll out gradually and reuse the same foundations for the next workflow