Heuristics for AI agent orchestration in 2026
Like so many things in life, the rule of thumb is “as simple as possible but no simpler”. More agents means less simplicity. Agents talking to each other more means less simplicity.
The amount of complexity which can be handled is limited by the AI’s context window and your own (ie the amount of stuff you can hold in your head). Increasingly the majority of the context window sits with the AI.
A planning step before execution is probably the single best thing you can do. It costs little in tokens. It improves implementation and it increases your understanding of what the model is doing.
With 2026's token windows, many problems that look like they need multiple agents are actually single-agent problems with good context management. Before reaching for multi-agent orchestration, ask: "could one agent with all the relevant information in its context window do this?" Often yes. That doesn't mean you shouldn't use multi-agent team - they can be great - but the bar to do so shouldn't be too low.
The easier a job is to verify, the further you can let your agents go nuts. If you need to verify in more detail, eg if they are developing something truly novel, then don’t let them get far ahead of you. The same is true if the cost of failure or error is low.
The more generic the structure of your agent team, the less oversight you need to give to the architecture of the team itself. I.e. you can delegate more of the design of the team to the lead agent.
Teams of subagents: good when multiple tasks to be done which can be handled in isolation. Similar to how you might break a large dev project into different elements to build (UI, server, special ML gizmo, etc)
Teams of agents: good when need more peer review or in-depth QA or some other form of interdependence - when you need the agents to interact more, eg when more than go wrong during implementation
If you need to articulate how things work for customers, YOLO agent runs are less amazing. Again, this favours simplicity.
Think of agents as you might developer staff you can delegate to. Ask probing questions of their understanding and implementation choices.
Instruct your agents to use scratchpads, eg markdown files. Keep these and other instructions in the file the model uses for it’s core instructions, eg CLAUDE.md for Claude Code.
Officially you're not supposed to give your agent all permissions for everything, but in practice in Claude Code, this works out pretty well (so far!)