AI Agent

An AI agent is software that works out how to reach a goal, instead of following a script someone wrote in advance. Give a traditional automation the instruction "email every customer whose invoice is overdue" and it does exactly that, in exactly that order, forever. Give an agent the goal "chase our overdue invoices" and it can look up who's overdue, decide that two of them already paid yesterday, draft different messages for a first reminder versus a fourth, and escalate the one that's ninety days late.

The difference is who does the deciding. Automation is a train on rails — fast, reliable, and it only goes where the track goes. An agent is a courier with an address: it picks the route, and it can re-plan when a road is closed.

Why AI agents matter for your business

Most business processes aren't a straight line. They involve judgement calls — is this enquiry urgent, does this invoice look wrong, is this the same customer under a different name? Those calls are exactly where traditional automation gives up and hands the work back to a person.

Agents matter because they can cover that middle ground:

• They handle variation. A rules-based system needs a rule for every case. An agent can reason about a case it hasn't seen before.
• They use tools. A useful agent doesn't just generate text — it can query your database, call an API, or file a ticket. The reasoning is only valuable because it's connected to something.
• They work in steps. Complex jobs get broken down, attempted, checked, and retried, rather than failing at the first surprise.

Where agents are the wrong answer

Agents are slower, more expensive, and less predictable than plain automation. If your process genuinely is a straight line — "when a form is submitted, add a row to the spreadsheet" — an agent is a worse solution than a webhook, and it costs more to run.

The honest test is whether your process needs judgement. If you can write the rules down completely, write the rules down. If your team's answer to "how do you decide?" is "it depends, you get a feel for it" — that's where an agent earns its cost.

The other real constraint is trust. An agent that can act on your systems can act wrongly on your systems. The agents worth deploying are the ones with clear boundaries on what they may touch, and a human check on anything expensive or irreversible.

How we use agents

We built a video-to-map agent framework for The Munch Map that processes social media content, enriches it with multi-dimensional metadata, and makes it searchable — work that would have taken weeks of manual data entry per creator. For Biosense, agentic pipelines accelerate root cause analysis in scientific workflows, cutting up to 90% of the manual workload.

In both cases the agent isn't the product — it's the thing that makes the product possible at a scale a person couldn't match.