Running a single AI agent is relatively straightforward. Running ten agents in parallel, each working on different parts of a shared system, while avoiding conflicts, sharing relevant context, and respecting human oversight — that's an engineering problem of a different character.

The coordination problem

The naive approach to multi-agent systems is to give every agent access to everything: shared memory, shared tools, shared context. This is tempting because it seems to maximize information availability. In practice, it causes serious problems.

When agents in different domains — coding, marketing, customer support — share the same memory pool, they accumulate context that actively interferes with each other's work. A content strategy insight pollutes a debugging session. A support pattern contaminates a refactor. The signal-to-noise ratio degrades with every agent added.

Isolation as a feature

The insight we built Grain around is that agent isolation is not a constraint to work around — it's a design goal. Agents should share memory within domains of related work, not globally.

This changes how coordination works. Rather than agents directly sharing state, coordination happens through explicit handoffs: a coordinator agent receives a task, breaks it into subtasks, assigns them to domain-appropriate agents, and synthesizes the results. The coordinator knows about the high-level work; the domain agents know about the detailed execution.

Parallelism and conflict

When multiple coding agents work on the same codebase simultaneously, they will conflict at the file system level if not managed carefully. The solution we adopted was to give each agent its own isolated working environment — so that two agents can edit the same logical codebase in parallel without stepping on each other's changes.

This adds overhead, but it makes concurrent work safe. It also creates a natural checkpoint: before any agent's changes are incorporated into the shared codebase, they must pass through a merge step that can surface conflicts explicitly.

Quality gates before human review

One of our strongest learnings was the value of automated quality gates between agent completion and human review. An agent that produces a change with a type error, a failing test, or a lint violation should be flagged before a human ever looks at it.

These gates don't require AI — they're standard software engineering checks. But running them automatically before escalating to human review dramatically reduces the noise in the review queue and lets human attention focus on real decisions.

Cost visibility

Running many agents across many tasks means running many LLM API calls — and costs can accumulate faster than expected. We found that granular, real-time cost tracking is essential infrastructure, not a nice-to-have. Without it, teams lose track of which agents and which task types are consuming disproportionate budget, and optimization becomes guesswork.