Use case

AI Agents for Backend Development

Name: Fleet
Author: Fleet

Backend development tasks often divide cleanly into two categories: well-specified implementation work (add an endpoint, extend a schema, wire up a new service) and design work (decide how to structure the data model, choose the right abstraction, handle the edge cases no one thought of). The first category consumes most of a senior engineer's time even though it does not require their judgment.

The bottleneck is that a senior engineer is required for both categories. Agents cannot replace judgment on system design, but they can execute well-specified implementation tasks, freeing senior engineers to spend their time on the work that actually requires them.

How it works with an agent fleet

A backend developer agent is assigned implementation tickets. It works on a branch, writes the implementation and tests, opens a PR, and waits for review.

agents:
  - name: backend-dev
    role: backend-developer
    model: claude-sonnet-4-6
    subscribes_to: ticket_ready

The agent's prompt lives at .fleet/prompts/backend-dev.md — Fleet resolves it by convention from the agent name, so there is no prompt: field in config. The agent prompt describes your backend stack, coding conventions, test patterns, and API standards. When a ticket has sufficient detail, the agent can work through the implementation without human guidance.

The fleet pattern

Backend developer agents pick up tickets from the ready queue. They work independently on separate branches. A tech-lead reviews PRs for architectural fit. The PM or PO watches fleet log for completion events and updates the roadmap. Senior engineers focus on system design and review rather than implementation.

Guardrails that matter here

Developer agent cannot merge — all implementation goes through tech-lead review
Ticket must have sufficient detail before an agent starts — agents publish a blocker event for underspecified tickets rather than guessing
Per-agent run-time (duration) budget prevents unlimited time on a single task; if the budget is hit, the agent reports partial progress and waits for human guidance

Who this is for

Engineering teams with a well-defined backend stack and a backlog of specified implementation work. The agent needs clear tickets: defined inputs, outputs, and acceptance criteria. Agents do not replace product discovery or system design.

Frequently asked questions

What backend stacks do the agents support?

Agents use Claude Code, which handles most mainstream languages and frameworks. You configure the build, test, and lint commands in the agent prompt. The agent follows whatever stack you describe.

Can agents work in parallel on different tickets?

Yes. Fleet starts one agent per ticket. Multiple developer agents can run simultaneously on separate branches. The concurrency limit is configurable per agent via `max_concurrent`.

Related use cases

AI Agents for Frontend Development →AI Agents for Test Generation →AI Agents for DevOps and CI →

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