What is agentic AI? AI agents explained, with examples by industry

What is an AI agent?

An AI agent is software that is given a goal, works out the steps to reach it, and takes those steps by using tools: it reads documents, searches records, calls systems, fills in forms and drafts what a person would otherwise write. It checks what came back after each step and decides what to do next, instead of following a script written in advance.

Most agents today are built around a large language model, which does the reading, reasoning and writing. What turns a model into an agent is everything around it:

• A goal and instructions: what done looks like, the rules to follow and when to stop and ask.
• Tools: the systems, searches and actions the agent is allowed to use, and nothing else.
• Context: the documents, records and history it needs for this task, retrieved as it works.
• Checks: tests on its own output, such as whether every figure ties back to a source page, before anything moves on.
• A person who decides: the step where a named human reviews the evidence and approves, edits or rejects.

What makes AI "agentic"?

Agentic describes how much of the path the AI chooses for itself. The OECD's definition of an AI system, which the EU AI Act's own definition closely follows, says that different AI systems vary in their levels of autonomy and adaptiveness after deployment. A spell checker has almost none; an agent that gathers the documents for a loan file, notices one is missing, asks for it and then drafts the credit memo has a good deal more.

Anthropic, a model provider, draws a useful line in its engineering guidance: in a workflow, models and tools follow predefined code paths; in an agent, the model directs its own process and tool use. Real business systems usually mix the two. The order of the work and the approval steps are fixed, because an auditor needs them to be, while the reading, matching and drafting inside each step is left to agents.

Agentic AI therefore means a system of one or more AI agents that carries a piece of work from the input to a result someone can act on. In serious deployments it is a crew of narrow agents, each with one job (sort the documents, extract the figures, test them against the rules, draft the summary), rather than one agent asked to do everything.

How is agentic AI different from a chatbot or generative AI?

A chatbot answers the question it is asked and then waits. Generative AI, in the narrow sense, produces content: a paragraph, a summary, an image. Both are useful, and agents use generative models inside them.

The difference is that an agent finishes a task. Asked to review a vendor's security questionnaire, a chatbot can explain what a SOC 2 report is; an agentic system reads the questionnaire, finds the answer to each question in the company's approved policies, drafts every reply with its source, flags the ones it could not answer and hands the set to the security lead to approve. The output is work done, with the evidence attached, not text to copy and paste.

Agentic AI vs RPA and workflow automation: what is the difference?

Robotic process automation (RPA) records and replays the clicks and keystrokes a person makes in an application. It is fast and cheap when screens, fields and rules never change, and it breaks when a screen moves or a document arrives in a layout nobody planned for. Workflow automation tools route tasks between people and systems on rules someone wrote, and they need structured data to route on.

Agentic AI handles the part those tools leave to people: reading documents that vary, judging which rule applies, explaining an exception and drafting the response. It does not replace a workflow engine or a system of record, and it should not. The common pattern is that the workflow keeps the steps, the deadlines and the approvals; agents do the reading and reasoning inside each step; and RPA or an API moves the approved result into the system that needs it.

• Inputs: RPA and workflow tools need structured data or fixed screens; agents read emails, PDFs, scans and spreadsheets in any layout.
• Rules: RPA follows the exact steps it was given; agents apply written policy and explain which clause they used.
• Exceptions: RPA stops and queues them for a person; agents investigate them and propose a resolution for a person to approve.
• Change: a new document layout breaks a template or a bot; an agent reads it, and accuracy is measured to prove it still does.
• Evidence: RPA logs the clicks; a well-built agentic system records what it read, what it concluded, the source behind each conclusion and who approved it.

Botminds compared with UiPath →

What does human in the loop mean for AI agents?

Human in the loop means a named person reviews the agent's work and makes the decision at the points that matter: approving a loan, paying or denying a claim, filing with a regulator, releasing a batch, sending money. The agent prepares the decision and the evidence; the person makes it and the system records who made it, when and on what basis.

This is not only good practice. For AI systems the EU AI Act classes as high-risk, Article 14 requires that they be designed so people can oversee them effectively, and that the people assigned to oversight be enabled to understand the system's capacities and limits, stay alert to over-reliance on its output, and disregard, override or reverse that output. Even where no rule applies, the same design is what lets a reviewer trust the work: every figure links to the page it came from, every finding names the rule it applied, and nothing irreversible happens without a sign-off.

A useful test when you evaluate any agentic product: ask to see the screen where the person decides, what evidence they see there, and the audit record that is left afterwards. If the answer is a confidence score and an Approve button, the human is not really in the loop.

Agentic AI examples in lending, banking and investment management

Lending runs on documents that arrive in every layout: tax returns, audited statements, appraisals, pay stubs, compliance certificates. Agents spread the financials for every related entity with each number linked to its source line, draft the credit memo from the spread, re-test covenants when a certificate arrives, and turn a decline into an adverse action notice with specific reasons. Investment teams use the same pattern to answer questions across filings, broker research and their own notes with every answer cited, to check before a trade which funds may buy a security under their guidelines, to reconcile positions and cash with custodians and explain each break, and to draft client reports for a portfolio manager to approve.

A worked example, from the sample case on the credit memo solution page. The input: a commercial borrower asks its bank for $19 million across three facilities, the analyst has approved the spread, and the deal file holds 38 documents and 412 pages. What the agents do: a file reader indexes every figure with its page; a cash flow analyst computes coverage, debt service and stress cases from the spread version the analyst approved, and never changes it; a memo writer drafts each section of the bank's own memo template with every number cited; a policy checker lists each exception with its policy section, the actual figure, the limit and who approves it; and a risk grader proposes a grade with the reason for each factor.

Where the person decides: when a guarantor's personal financial statement and brokerage statement disagree on liquid assets, the agents use neither figure; the analyst chooses the verified one and records why. The analyst accepts every change the agents make, the agents never waive a policy exception, and the grade stays a proposal until the approver sets it. What is recorded: every edit, by an agent or a person, goes on a change log with its before and after values, and each version of the memo is kept for the credit file.

• Commercial Financial SpreadingA spreading solution for credit teams that turns tax returns and audits into the bank's spread.
• Credit Memo AutomationA memo solution for credit analysts that drafts the full credit memo from the spread and file.
• Covenant MonitoringA covenant solution for lenders that collects certificates and re-tests every covenant.
• Adverse Action Notice AutomationA compliance solution for consumer lenders that turns each decline into a notice with reasons.
• Investment Research AssistantAnswers investment teams' questions across filings, broker research and notes, all cited
• Pre-Trade Compliance ChecksTells portfolio managers which funds may buy a security and how much, quoting the clause
• Position and Cash ReconciliationReconciles books to custodian and prime broker files and explains every break for middle office
• Institutional Client ReportingAgents draft, check and release monthly and quarterly reports for every institutional mandate

Buyer's guide: AI for lending →AI in banking and financial services →Buyer's guide: AI in asset management →

Agentic AI examples in insurance

In insurance, agents read a broker submission and key it for clearance, turn prior-carrier loss runs into one loss view, match a first notice of loss to its policy and route it by severity, and check whether a loss is covered by quoting the form and endorsement. Underwriters and adjusters keep the decision; the agents remove the reading and re-keying around it.

A worked example, from the sample case on the claim coverage verification page. The input: a homeowner reports a slow leak under the kitchen floor in an 11-minute first-notice call, followed by a plumber's report and 14 restoration photos. What the agents do: a policy assembler pulls the declarations and the exact edition of every form and endorsement in force on the date of loss; a fact reader writes down each fact with its source and turns missing facts into open questions; a coverage mapper turns the forms into a decision tree of insuring agreement, perils, exclusions and endorsements; a coverage analyst walks the recorded facts through the tree, quotes the wording each step turned on and computes the amounts; and a letter drafter writes the coverage letter in plain words. Here the path runs into the exclusion for seepage lasting 14 days or more and back out through a hidden water damage endorsement, so the draft position is covered in part.

Where the person decides: the agents never deny coverage, and any “not covered” goes to a person. The desk adjuster signs the position, and because the payment is more than $10,000 below the amount claimed, a claims supervisor co-signs before the letter and the payment go out. What is recorded: the position, its path through the policy, the facts and the edition of every form are kept with the signatures, and each agent step is on the activity trail.

• Commercial Submission Intake & ClearanceAn intake solution for commercial carriers that reads, keys and clears every broker submission.
• FNOL & Claims Triage AutomationAn intake solution that reads each first notice, matches its policy and routes it by severity.
• Claim Coverage VerificationA coverage analysis solution that says if a loss is covered, quoting the form and endorsement.
• Subrogation IdentificationA subrogation solution that reads new and closed claims for another party who should pay.

Buyer's guide: AI for insurance →Insurance claims automation →AI underwriting →

Agentic AI examples in the back office, legal and life sciences

Every company has the same back office, and it is where agentic AI is easiest to measure. Agents resolve invoices that failed the three-way match, reconcile balance-sheet accounts to cited support at month end, review incoming NDAs against standard positions for counsel to approve, and draft answers to security questionnaires from approved policy. Life sciences work is regulated writing and checking: agents draft clinical study reports from the tables in the sponsor's template, draft answers to health authority questions with each statement sourced, check promotional material for unsupported claims before medical, legal and regulatory review, and gather the evidence for a GMP deviation so quality assurance can confirm the root cause.

A worked example, from the sample case on the invoice exception management page. The input: an accounts payable hub serving five entities has 137 blocked invoices, 14 of them from one supplier that has said it will stop deliveries if too much stays overdue. What the agents do: a cause finder groups the blocked invoices by the cause they share and traces this one, citing the clause, to a contract amendment that raised three prices by 3.2% while the purchase order still carried the old prices; a resolver weighs the possible fixes, proposes asking the buyer to correct the purchase order and explains why it ruled out a credit note; and a separate checker re-runs the three-way match from the ERP data, never from the resolver's reasoning, and finds all 14 lines match after the fix.

Where the person decides: because the variance is over the matching tolerance, the accounts payable lead signs the fix with a reason code, and variances above $25,000 go to the group controller. The agents never change a purchase order, receipt or invoice themselves: the buyer posts the corrected purchase order under their own name, the checker re-matches it, and only then are the invoices released to the payment run. What is recorded: every action, by a person or an agent, goes on an audit trail with its evidence and a chained record id, and an evidence pack for the auditor holds the decisions, source documents and checker results.

• Invoice Exception ManagementAgents resolve three-way-match failures, price and quantity variances and blocked invoices
• Account Reconciliation AutomationReconciles balance-sheet accounts to cited support for controllers and close teams
• NDA Review AutomationTriages and reviews incoming NDAs against standard positions; counsel approves or escalates
• Security Questionnaire AutomationDrafts cited answers to inbound security questionnaires (SIG, CAIQ) for sales and GRC
• Clinical Study Report AuthoringA clinical study report solution for medical writers that drafts results from the tables.
• Health Authority Query ResponseA regulatory affairs solution for submission teams that drafts answers to regulator questions.
• MLR Review AutomationA promotional review solution for MLR teams that checks claims, references and fair balance.
• Deviation & CAPA ManagementA GMP investigation solution for QA that gathers evidence and proposes root causes and CAPAs.

Buyer's guide: AI for back-office and enterprise work →AI contract review →Buyer's guide: AI for life sciences →

Agentic AI examples in airlines, utilities and education

Airlines run on tight clocks: agents build recovery options for the operations control center when weather or an outage breaks the schedule, track every deferred defect against its deadline, work out which refunds the DOT's automatic refund rule requires so the refund team can issue them on time, and keep airworthiness directives current across the fleet. Utilities use agents on the customer side and the asset side: they clear billing exceptions with an evidenced correction, explain a high-bill complaint from the meter data, triage meter data exceptions and propose the fix, and check solar and storage interconnection applications against the utility's requirements for an engineer to sign. Colleges and school districts carry heavy compliance paperwork with small teams: agents score admissions files against the reading rubric with every claim cited to the page, cross-check FAFSA verification documents, audit degree requirements before conferral and check each draft IEP for required content and dates, while staff make every decision about a student.

A worked example, from the sample case on the airworthiness directive tracking page. The input: the FAA publishes a directive, effective on publication, on cracking in the slat tracks of one aircraft type, and the airline flies 300 aircraft. What the agents do: a watch agent finds the directive in the Federal Register and fetches the service bulletin it incorporates; a reader agent extracts applicability, required actions and compliance times, quoting each one; an applicability agent runs the 62 aircraft of that type through each applicability paragraph in turn, using configuration, installed part numbers and work history; a check agent recomputes the answer from the raw records without seeing the first one, and refuses credit for an earlier inspection because a scanned work pack is missing its result sheet, which brings one more aircraft in, to 14 of 62; and a planner agent finds a maintenance slot ahead of each aircraft's due point.

Where the person decides: below a set confidence the applicability agent asks the engineer instead of deciding, and any difference between the two agents blocks the call until a person settles it. The airworthiness directive engineer signs the applicability call with a reason code, the maintenance planner accepts each slot, technical records confirms each closure, and the director of quality signs the status report. What is recorded: every agent step and every decision goes on a chained audit trail that exports for the FAA.

• Airline Disruption ManagementA crew of agents builds recovery plans for the OCC when weather or IT breaks the schedule
• Airworthiness Directive Compliance TrackingReads each new AD, decides which tails it applies to and proves compliance, for engineers
• Deferred Defect ManagementTracks every MEL deferral clock and plans each fix before it expires, for maintenance control
• Airline Refund AutomationDecides and issues DOT-required automatic refunds on time, with the reason, for refund teams
• Billing Exception ManagementA workflow solution for billing analysts that clears held bills with an evidenced correction.
• High Bill InvestigationA workflow solution for billing teams that explains each high-bill complaint from meter data.
• VEE Exception ManagementA workflow solution for MDM analysts that triages VEE exceptions and proposes the fix.
• DER Interconnection Application ReviewA workflow solution for interconnection engineers that checks solar and storage applications.
• Holistic Admissions ReviewAdmissions readers get each file scored against the rubric, every claim cited to the page
• FAFSA VerificationAid officers cross-check ISIRs, tax data and household forms and clear conflicting data
• Degree Audit & Degree ConferralRegistrars audit requirements, substitutions, honors and diploma names before conferral
• IEP Compliance ReviewChecks each draft IEP for required content, measurable goals and dates, for special ed staff

Buyer's guide: AI for airlines →Buyer's guide: AI for utilities →Buyer's guide: AI in education →

What can go wrong with agentic AI, and how do you govern it?

Agents inherit the weaknesses of the models inside them and add a few of their own. NIST's Generative AI Profile names confabulation, the confident statement of something false, as a core risk of generative models; an agent that acts on a confabulated fact can do more harm than a chatbot that merely states it. Agents can also take an action that was allowed but unwise, run up cost in loops, or expose data to a tool that should not see it.

The NIST AI Risk Management Framework, a voluntary framework, organizes the response into four functions: govern, map, measure and manage. In practice that comes down to a handful of controls you can ask any vendor to show you:

• Every output linked to its source, so a reviewer can check a claim in one click instead of trusting it.
• Agents limited to the tools and data their task needs, with role-based access for the people around them.
• Accuracy measured on your own documents before go-live and monitored after, field by field.
• Hard stops: nothing that moves money, files with a regulator or reaches a customer without a named approval.
• A complete audit record of every agent action and every human decision, which cannot be edited afterwards.
• An inventory of every agent and model in use, who owns it and when it was last tested.

How do you start with agentic AI?

Start with one process that is document-heavy, already reviewed by a person, and measured today, so the review stays and the reading goes. Take a baseline of time per case, touch time and error rate from the current process, run the agents on your own recent cases (including the messy ones) for a fixed window, and compare. Decide before you start what result would justify going further.

Ask early how the vendor deploys and whether your data is used to train its models; Botminds sets out its answers on its security page. Whichever vendor you choose, plan for your own third-party and model risk reviews from the first week, not the last.

See every industry's solutions →How the platform builds and governs agents →How Botminds secures your data →

A checklist for any vendor

1. Can you show me the step where a person decides, and what they see there?A good answer is a live screen with the agent's draft, the source evidence and the rule applied, plus the audit record left after the decision.
2. Does every value and finding link to the exact page or record it came from?Citations let a reviewer check work in seconds and are the main defense against confident errors.
3. Which tools and data can each agent use, and who sets that?Look for least-privilege access per agent and per task, set by your administrators, not a single broad service account.
4. How is accuracy measured on our documents, and how is it monitored after go-live?Ask for field-level error rates on a sample of your own cases, and for the report you will see each month after launch.
5. What can an agent never do without approval?Payments, regulatory filings, customer messages and anything that cannot be undone should require a named approval.
6. What happens when an agent is unsure or a document is new?A good system says what it could not find and routes the case to a person, rather than guessing.
7. Where does our data live, and is it used to train models?You should get a clear deployment choice, encryption details, and a written commitment on model training.
8. How do we change a rule or a template ourselves?Policies change; you should be able to update an agent's rules without a new project, and see which cases the change affects.

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