Customer support
Classify requests, look up orders and accounts, resolve routine cases and pass complex ones to staff with a full summary.
PRODEV Info Solutions designs, integrates and runs AI agents that take a goal, plan the steps and complete the task across your business systems. Each agent works within limits you approve, keeps a record of what it did and hands over to a person when it should.
Agent-as-a-Service (AaaS) is a delivery model in which AI agents are provided as a managed, subscription-style service. Instead of giving your team another tool to operate, an agent takes a goal, plans the steps, uses your business systems and completes the task, escalating to a person when it needs to.
The short version: with traditional software, your people operate the tool. With AaaS, your people supervise an agent that does the work.
Agents suit processes with clear goals and a defined set of tools, where the steps vary too much for fixed automation.
Classify requests, look up orders and accounts, resolve routine cases and pass complex ones to staff with a full summary.
Qualify new leads, enrich and update CRM records, schedule follow-ups and prepare your salespeople for each conversation.
Read invoices and forms, match them against your records, enter the data and raise anything that does not reconcile.
Triage tickets and alerts, collect diagnostics, run approved runbook steps and escalate with the evidence attached.
Coordinate joining documents, account requests and reminders, and answer common policy questions from approved sources.
Watch your metrics and data feeds, investigate unusual changes and send people a short explanation, not just an alert.
AaaS is an ongoing service: the agent is designed, connected, supervised and improved, not handed over and forgotten.
We map the process, define the agent's goal and agree where it must stop and ask a person.
We connect your systems through APIs, webhooks and connectors such as the Model Context Protocol (MCP).
Each agent receives its own scoped access, with spending, volume and action limits.
Where a process needs it, specialised agents work together, with a clear hand-off between each stage.
Every run leaves a trace of its plan, tool calls and results, so behaviour can be inspected and explained.
We monitor failures and cost, respond to incidents, and adjust the agent as your process changes.
These technologies overlap, and an agent often uses the others as tools. The key difference is who decides the next step.
| SaaS | RPA | Chatbot | AaaS | |
|---|---|---|---|---|
| What it does | Gives people a tool to work in | Repeats fixed, scripted steps | Answers questions in a conversation | Pursues a goal and completes the task |
| Who decides the next step | A person | The script | The conversation flow or the user | The agent, within agreed limits |
| When something unexpected happens | A person handles it | It usually stops or fails | It often hands over or gives a generic reply | It re-plans, tries another route or escalates |
| Works across systems | Mostly within its own product | Yes, along fixed paths | Often limited | Yes, through permitted tools |
| Main oversight needed | Training and user management | Script maintenance | Reviewing answer quality | Permissions, logs and exception handling |
| Typical pricing shape | Per seat | Per bot or per licence | Per seat or per conversation | Service fee plus usage, sometimes tied to tasks or outcomes |
Autonomy should be earned rather than switched on. We move an agent up a level only when its logs and results show it is ready, and you decide each step.
The agent prepares the action. A person does it.
The agent carries out the action after a person approves it.
The agent acts alone on low-risk cases and escalates the rest.
The agent runs the process and people review results and exceptions.
We begin with a single process and a low level of autonomy, then expand once the evidence supports it.
We document the real workflow, including its exceptions, its systems and the people responsible.
We agree the agent's goal, permitted tools, action limits and escalation rules.
We build the agent and connect it to your systems using dedicated, least-privilege accounts.
The agent works alongside your team without taking live actions, so its decisions can be compared with theirs.
We launch with approvals on, monitor every run and widen autonomy only as results justify it.
An agent that can act can also make mistakes, so controls sit outside the AI model, where its output cannot talk its way around them.
Each agent has its own account and only the permissions its job requires.
Sensitive or irreversible actions wait for a person, however confident the agent is.
Emails, web pages and documents are treated as untrusted data, never as instructions.
Plans, tool calls, results and approvals are recorded for review.
Rate, spend and volume limits apply, and an agent can be paused immediately.
Scenario tests run before any model, prompt or tool update reaches production.
We use the OWASP Top 10 for LLM Applications and the NIST AI Risk Management Framework as design references. They are public guidance that we follow, not certifications that we hold.
Last updated 6 November 2026. Also see our AI Solutions and Data Science services.
Describe one repetitive process that crosses several tools. We will tell you honestly whether an agent is the right answer or whether simpler automation would serve you better.
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