          Use cases/Software team
        

01 · Software team use case

Adding agents to an existing software team.

A practical model for teams with a large backlog, too many services, and not enough delivery capacity. Cloud28.ai adds planning, implementation, QA, documentation, review, and release support around the customer's existing architect and product lead.

Team 12 engineers Estate 90+ services Run-rate $30K → $10K

Why this team needed Cloud28.ai

Real engineering capacity, spent on coordination.

The team had real engineering capacity, but too much of it was spent coordinating service sprawl, recovering context, writing late documentation, and preparing releases after implementation was already underway.

Cloud28.ai does not replace product ownership. It adds agents into the existing team workflow so planning, implementation support, QA evidence, documentation, review preparation, and release handover become a repeatable lane around the customer's senior people.

Client profile

Software organization with a 12-person engineering team and a large microservice estate.

Workflow

Backlog planning, ticket flow, implementation, service consolidation, testing, documentation, and release management.

Scale

90+ microservices, two-week sprint cycle, one release every two weeks, partial documentation, and uneven test coverage.

Target model

Keep product and architecture decisions with the customer while agents move planned work through delivery gates.

01 · Current operating problems

Where delivery risk was accumulating.

Area	Current pattern	Delivery risk
Backlog	Jira captures work, but planning detail often arrives late.	Engineers start before acceptance criteria, risk, and test paths are clear.
Architecture	A wide service estate creates many ownership boundaries.	Small changes require coordination across too many code paths.
Documentation	Coverage is partial and often trails the code.	New work starts with rediscovery instead of reusable context.
Testing	Implementation tests and release tests are inconsistent.	Release confidence depends on manual inspection and memory.
Release cadence	Two-week sprints create batching pressure.	Small changes wait for larger release windows.

02 · The Cloud28.ai delivery layer

Delivery roles around your senior people.

Cloud28.ai adds a delivery layer to the existing workflow. The customer still owns product priority, architecture decisions, source control, and release approval. Agents help prepare the work, implement scoped tasks, produce QA evidence, update documentation, and package handover notes.

Delivery role	What it does	Human control
Planning	Turns backlog items into scoped tickets, acceptance criteria, risk notes, and test paths.	Product manager approves priority and scope.
Implementation	Works on defined frontend, backend, connector, or automation tasks.	Customer engineer reviews code and architecture impact.
QA	Creates checks, regression notes, and release evidence.	Release owner approves the final quality gate.
Documentation	Updates runbooks, service notes, and handover material as work ships.	Team lead confirms operational accuracy.
Release	Prepares release notes, rollback notes, and deployment checklist.	Human owner authorizes production movement.

03 · Target operating model

How the team runs after Cloud28.ai.

Move from broad sprint batching to weekly flow where work-in-progress, blockers, review status, and release readiness stay visible.

Consolidate service ownership where practical, so the team is not paying coordination tax across dozens of small services.

Treat documentation and test evidence as delivery gates, not cleanup tasks after the work is already shipped.

Use agents for repeatable delivery work while keeping architecture judgment and product tradeoffs with senior humans.

04 · Measures to track

Baseline today. Target signal after.

Measure	Baseline	Target signal
Planning completeness	Tickets vary in scope and readiness.	Tickets enter build with acceptance criteria, owner, risk, and test path.
Documentation coverage	Partial documentation across services.	Docs update as part of each shipped change.
Release evidence	Manual checks vary by engineer and release.	Every release has test evidence, notes, and rollback path.
Cycle time	One release every two weeks.	Smaller releases can move multiple times per week when risk is low.
Team focus	Senior engineers coordinate too much routine delivery work.	Senior engineers focus on architecture, review, exceptions, and product-critical code.

Fit-dependent outcome

The $30,000 → $10,000 monthly run-rate example is a model, not a universal guarantee. It applies when the customer's backlog is well-scoped, access is ready, and agents can take over repeatable delivery work without hiding product or architecture decisions.

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