Cutting through the hype: what do AI agents actually mean for agencies?
Summary: AI agents are one of the loudest topics in AI right now - with some leaders claiming that agents can now run their business for them. We look at what agents actually mean for agencies, why humans still need to stay in control, where to use them first, and why the smartest applications often sit around the valuable work rather than replacing it.
Author: Asta Vallis
Read length: 5 minutes
Date: 26.08.26
Spark AI is the AI performance partner that builds organisation-wide AI capability for creative agencies and brand teams. We take ambitious teams from fragmented experimentation to AI embedded across their people, processes and systems ā so it delivers real commercial impact.
Talk about AI agents has taken over LinkedIn feeds and podcasts. It can sound as though every agency should already have autonomous systems running their business. During a recent Spark Session, Jules Love, co-founder of Spark AI, cut through the hype and discussed what agents actually are, where they are useful, and how agencies can deploy them responsibly.
Firstly, what are agents?
In practical terms, an agent is a narrowly defined AI workflow that can act on a trigger, connect to other systems, and produce or update something outside the chat. It might monitor a CRM or compile a report. If you want to know about the 6 steps that make up an agent, read this blog.
The goal should not be to remove people from the work entirely. It should be to give AI enough freedom to handle repetitive or operational parts of a workflow while keeping human judgment where it matters.
Why do humans still need to stay in the loop?
While agents can produce useful outputs, they still need human oversight.
The important thing is to keep your brain switched on. At Spark, we preach for Think - AI - Think. Use your own judgement before and after AI.
This matters because humans are prone to automation bias: we tend to trust what a computer generates more than ourselves. That risk can be even greater with LLMs, because they can produce polished, confident-looking outputs that feel more reliable than they actually are.
Where should agencies use AI agents first?
When agencies begin thinking about agents, there can be a temptation to point them immediately at the most valuable piece of work in the business. Instead, point it at the useful, lower-risk jobs surrounding the valuable work.
Examples of useful, low risk agents to try out:
Competitors analysis emailed to you every week
An agent that monitors your CRM and highlights which deals need attention
A morning briefing prepping you for the day.
Meeting notes into actions
These remove is the administration, searching and chasing information that sits around that person.
Should you automate the thing your client is paying you for?
Usually, no. One of the most useful examples from our Spark Session came from a copywriting agency using agents across its delivery process.
Its rule is simple: all of the copy is still written by a human.
Instead, the agency has pointed agents at the work around the writing.
When a brief arrives, an agent reviews it against the agency's briefing template and examples of strong briefs, identifies any gaps and asks the client for the missing information.
Once the brief is complete, research agents gather relevant external information and pull together knowledge about the client.
The copywriter therefore begins with a much stronger brief and research pack rather than spending a large part of their time gathering it themselves.
When the copy is complete, another agent provides an initial critique against the brief, client guidelines and examples of previously approved work.
The writer still decides whether the feedback is useful and whether to act on it.
The result is that the writer spends more time doing the part of the job they are best at ā and the part the client is actually paying for. In this example, the agency is achieving 95% first-time sign-off on its copy.
Frequently asked questions
Where can Spark AI help me build AI Agents?
We have launched Agent Build Sprint. It is a two-day sprint where your team learns to spot the right tasks for an agent, then builds and tests on real work. A week later, we come back to refine what you've built, map what's next, and set you up to share it with the rest of your team.
Are AI agents autonomous?
They can operate with a degree of autonomy, but in most agency use cases they should still work within clear human-set boundaries. Agents can run on triggers, access connected systems and create outputs without someone manually prompting every step. That does not mean they should be given unrestricted authority to make consequential decisions.
What are good first use cases for AI agents in an agency?
Look for repetitive internal work that involves collecting, checking or moving information. Examples include monitoring a CRM, producing competitor or client updates, checking timesheets, turning meeting notes into actions, or preparing research, These applications can create meaningful time savings while keeping humans responsible for higher-value decisions.
