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Role · Operations

AI agents for operations teams

Operations is where agentic AI pays off first. Volume is high, documents arrive from everywhere, the rules are written down and the exceptions eat the day. An agent takes the routine part of each case and hands your team the exceptions with the groundwork done. Infosistema connects agents to the systems the work runs on, and that connection decides whether they get past the pilot.

At a glance

  • Infosistema connects AI agents to the case-management, document, ticketing, ERP and core systems that operations and back-office teams run on.
  • Typical first processes are document intake and classification, case preparation, exception handling, customer service operations and regulatory reporting.
  • Every engagement starts with a three-week analysis sprint that maps the processes, measures a baseline and recommends, per process, an agent, classic automation or not yet.
  • Agents move through four levels of autonomy (shadow, supervised, guided, autonomous) only when their results against the baseline justify it, with your approval.
  • Infosistema has built workflow and document systems for Santander Totta, Abrantes City Council and Obrecol and a pharmacovigilance system for Angelini, and implemented the contact-centre system of the Portuguese insurance supervisor (ISP).

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Which operations tasks can an AI agent take on?

Repetitive, system-heavy and rule-bound work with a steady flow of exceptions. In most operations teams, the same 5 processes come up.

Document intake and classification

The agent reads what arrives by email, upload, scan or post, classifies it, extracts the data and files it in the right case, asking the sender for whatever is missing.

  • Stays with people: unreadable or contradictory documents, and anything the agent isn't sure of.
  • Systems: inboxes, scanning, document management, case system.

Case preparation

Before a case reaches a specialist, the agent gathers what it needs from every system involved (history, contracts, external data) and writes a summary with the open points.

  • Stays with people: the decision on the case.
  • Systems: case or workflow system, CRM, ERP, external sources.

Exception handling

The agent investigates the transactions that fail a rule (mismatches, missing data, duplicates), fixes the routine ones under agreed rules and queues the rest with the evidence.

  • Stays with people: fixes above threshold and anything new.
  • Systems: core systems, reconciliation tools, data quality reports.

Customer service operations

The agent resolves requests end to end across CRM, ticketing and fulfilment systems: it checks the order or contract, acts and replies, escalating high-impact actions to a person.

  • Stays with people: complaints that need judgment, compensation and vulnerable customers.
  • Systems: CRM, ticketing, order and billing systems, contact-centre platform.

Regulatory and quality reporting

The agent collects the records a report needs, checks them for completeness, prepares the submission and keeps the evidence trail.

  • Stays with people: sign-off and submission.
  • Systems: case systems, quality management, regulator portals.

What makes agents hard to run in operations?

Scale and exceptions. An agent that's right 95% of the time on 10,000 cases a month still sends 500 wrong ones somewhere.

  • Baseline first. Without today's numbers (volume, handling time, error rate, backlog) no one can tell whether an agent helps. The sprint measures them for each candidate process.
  • Readiness is a chain. Consequence of an error, volume, structure and data each have to hold; the weakest one sets the outcome.
  • Classic automation is often cheaper. Where rules are stable and inputs are structured, a rule or a workflow costs less to run than an agent. The sprint says so when it applies.
  • Operating cost. An agent can cost more to run than to build. Monthly operation is estimated before the decision, with a cap on model consumption.

How does Infosistema start?

With a three-week analysis sprint on one operations area, for example customer requests or document intake.

  1. Week 1: map processes, people, systems and information flows, and short-list up to five candidates.
  2. Week 2: score each process for impact and readiness, and analyse it step by step (steps, decisions, exceptions, data, risks) against a baseline.
  3. Week 3: estimate build and monthly operation per agent, then recommend agent, agent with conditions, classic automation, or not yet.

Small agents on known pains start in supervised mode from week 2. Each month you get a report per agent: volume handled, quality against the baseline, exceptions, autonomy level and cost per completed task.

Which Infosistema products and partners help?

  • Vesster: six production agents for regulated processes (document collection, verification, fraud detection, external sources, orchestration and compliance), with graduated autonomy and an audit trail.
  • bizAPIs: official Portuguese company, tax and registry data as APIs, for verification steps.
  • Google CoE: Gemini Enterprise Agent Platform, Vertex AI and BigQuery for operations on Google Cloud.
  • AI Academy: an academy we build with you, inside your organisation, to qualify your teams in agentic AI, with learning paths by role.

What has Infosistema built for operations teams?

Santander Totta

Workflow Processes and Document Management

Instituto de Seguros de Portugal

Support Management System – Call-Center

Angelini

Angelini Pharmacovigilance

Obrecol

Debureaucratization and standardization of business processes

Abrantes City Council

Automation and standardization of business processes

Frequently asked questions

Where does agentic AI pay off first in operations?

In document-heavy, high-volume processes with written rules and frequent exceptions: intake, case preparation, exception handling and customer requests. The analysis sprint ranks your processes by impact and readiness.

What's the difference between an AI agent and RPA or a workflow?

A workflow or an RPA bot follows fixed steps; an agent can read unstructured input, decide the next step within rules and handle variation. Where the steps never vary, we recommend the workflow, because it's cheaper to run.

How do you stop an agent from making mistakes at scale?

It starts in shadow mode, then supervised, and moves up only when its error rate against the baseline is inside the tolerance agreed for that process, and it moves back down a level whenever quality drops below that line.

What happens to the people who did this work?

They handle the exceptions, supervise the agents and improve the process. The AI Academy prepares them for that, and the sprint maps which tasks change for whom.

How long until a first agent is working?

Quick wins on known pains can run in supervised mode from week 2 of the sprint. Larger agents are built after the sprint's decision, for the processes you approve.

Let's put AI to work in your operations

Tell us which process you'd like to look at first, and we'll reply within 1 business day.

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