Coordinate AI development across teams

Scale the system around the tools

One developer can coordinate a small prototype through direct attention. An organization cannot rely on one person remembering every service contract, release condition, and exception. AI increases the importance of making these relationships explicit.

Consider a fictional customer export that touches identity, billing, data, and platform teams. Each team can generate its own change quickly. The combined feature can still fail if they assume different customer identifiers or deployment sequences.

Treat the feature as a change across a system. Identify the shared contracts and the owner of each decision. DORA’s work on loosely coupled teams emphasizes the ability to work and release with limited coordination. This depends on architecture and working practices, not simply faster coding. DORA guidance.

Make shared contracts explicit

For the export, write down the customer identifier format, authorization semantics, API response, and compatibility period. Identify which team owns each contract. Define how consumers learn about a proposed change.

Prefer a compatible transition when clients cannot move together. Test the consumer’s expectation as well as the producer’s implementation. A service can pass its own tests while returning data that another team interprets incorrectly.

Avoid assigning every decision to a central committee. Put decisions with the team that owns the relevant consequence. Use shared constraints where inconsistency would create material risk.

Protect review capacity

Faster generation can increase the amount of work waiting for review. Large diffs, weak task briefs, and missing evidence make this worse. Adding more agents can increase the queue without improving release time.

Limit work in progress. Keep changes small enough for the available reviewers. Require a clear purpose, meaningful checks, and the relevant context before requesting review. Measure waiting time separately from active review effort.

Do not remove review controls merely to make the queue look shorter. First investigate repeated causes of review work. A shared test environment or a clearer platform interface may remove the cause more effectively.

Share useful context without sharing every secret

Publish current architecture constraints, interface contracts, approved patterns, and ownership information where teams and agents can use them. Give each item an owner and a review trigger.

Keep access appropriate to the task. A shared knowledge system should not automatically expose every customer record or security credential to every agent. Common guidance and unrestricted data access are different capabilities.

Measure accepted outcomes across the flow

Track the time from an accepted need to a usable change. Include failed attempts, rework, and incidents. Compare similar services and account for differences in risk and task complexity.

DORA’s 2025 research treats AI as part of an organizational system. Use that perspective to examine where increased generation helps and where it exposes a constraint. Research report.

A software factory becomes useful when it connects these responsibilities consistently: shared context, planned work, verified changes, controlled releases, and operational feedback. Evaluate that complete sequence when deciding how to scale AI development.