AI Agents
AI agents are not chatbots. A chatbot responds to questions. An agent takes action.
A well-built AI agent monitors a data source, reasons about what it finds, makes a decision, and does something — sends a message, updates a record, routes a request, flags an exception, drafts a document. It does this on a schedule or in response to an event, without someone initiating it.
The use cases that produce real value are specific. An agent that classifies incoming support requests and routes them to the right team. An agent that monitors transaction data for patterns that need review. An agent that drafts a structured report from raw data and sends it to the right person. An agent that watches for a trigger in one system and initiates a process in another.
These are not experimental use cases. They are operational tasks that currently require a person's time and attention, handed off to a system that handles them reliably.
_service.what_this_covers
What this covers
n8n agentic workflows
n8n's agent nodes allow workflows to apply reasoning — calling a language model to interpret data, make a decision, or generate output — and then act on that reasoning through the same automation infrastructure used for standard integrations. The result is a workflow that handles complexity and variation, not just predictable, structured inputs.
Copilot Studio agents
For businesses running Microsoft 365, Copilot Studio allows agents to be built inside the Microsoft ecosystem — connected to SharePoint, Teams, Outlook, and business data — without requiring external infrastructure.
Research and synthesis
Agents that gather information from multiple sources, synthesise it, and produce structured output: competitor monitoring, supplier research, regulatory updates, tender scanning.
Classification and routing
Agents that read incoming data — emails, form submissions, support tickets, documents — classify them by type, intent, or urgency, and route them to the right destination or trigger the right process.
Monitoring and alerting
Agents that watch data sources continuously and surface exceptions that need human attention — anomalies, threshold breaches, missing records, status changes.
Drafting and generation
Agents that produce structured written output from data inputs: reports, summaries, quotes, responses to standard enquiries.
_service.how_it_works
How it works in practice
AI agents are built as extensions of existing automation infrastructure, not as standalone tools. They connect to the systems where the relevant data lives, apply reasoning where reasoning adds value, and hand off to standard automation for the execution steps.
The work starts with identifying the specific task the agent will handle and defining what good output looks like. Agents built around vague objectives produce vague results. Agents built around specific, well-defined tasks produce reliable ones.
Is there a task your team does repeatedly that involves reading, reasoning, and acting?
Describe it. If an agent can handle it reliably, that will be clear within one conversation.
Describe the task