Team Topologies: is an AI agent a teammate or a platform?

If we start with the premise that a team has a limited mental capacity, what is the effect of adding an AI agent to that team? This question keeps bringing me back to Team Topologies by Matthew Skelton and Manuel Pais, a book that keeps coming up in discussions around platform engineering and SRE. The second edition, published by IT Revolution in 2025, includes case studies from ING and GovTech Singapore, and the afterword extends the concepts to AI-augmented organizations.

The premise is that since a team’s thinking capacity is finite, the work it owns should be sized to what it can carry. This leads to small, long-lived teams with boundaries drawn to minimize cognitive load. In chapter 3, the authors concede that there is no precise measure. “While there is no formula for cognitive load, we can assess the number and relative complexity (internal to the organization) of domains for which a given team is responsible.”

The model breaks cognitive load down into 3 types: Intrinsic (the difficulty of the problem itself), germane (the value-adding learning for the team), and Extraneous (all the bullshit the environment piles on, like “how do I deploy this again?”). The book wants to see the Extraneous engineered away so that there’s more room for the meaningful learning.

There are four team types built around reducing load. The stream-aligned team owns a work flow end to end, and the other three team types are mostly about relieving that load, either through a service they consume or through a temporary collaboration. The platform team is the clearest example of this.. their main job is to reduce the cognitive load of the stream-aligned teams who use the platform.

This book gets some pushback. Some people complain that organizations adopt the four labels but don’t adopt the conditions that make them work. Some people complain that platform teams become bottlenecks. Some people point out that politics and incentives are outside of the model. A recent thread on r/sre asked if a “team” is sometimes just a group of people who share a job title but work on unrelated epics.

This year, the discussion shifted to agents. At QCon London, Skelton argued that agents (like people) lose coherence when they operate outside a defined scope.. they need bounded agency. His question: why give an agent write access to every data store when no person would get it. Olivier Wulveryck mapped the four team types onto an agentic platform, where the platform absorbs technical complexity. Wulveryck also pointed out that agents give nearby humans “anticipation burden”.. since they respond instantly.

This made me think about how I use agents. I run one agent session per project, splitting responsibilities so each one stays small enough that I can keep up. It keeps things clean and aligns with how I think (more on that in Running an agent fleet like a small team). In the book’s terms, it’s a boundary decision about my own load as much as it is about the agents’.

I’m still not sure if an agent is a team member who needs a load boundary, a platform that lifts load off me, or just another tool. And does the answer change when the agent starts acting without asking first?