# Use Case: Adding Agents to an Existing Software Team

URL: https://cloud28.ai/use-case-software-team.html
Category: Software team
Reference outcome: monthly run-rate $30,000 → $10,000 (66.7% reduction; $240,000 annualized)
Owner/operator: Cloud Nine Software · Contact: sales@cloudninesoftware.vn

## Summary

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 — without replacing product ownership.

## Client Profile

- Team: 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, uneven test coverage.
- Target model: keep product and architecture decisions with the customer while agents move planned work through delivery gates.

## Why This Team Needed Cloud28.ai

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 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.

## Current Operating Problems

| 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. |

## Cloud28.ai Delivery Roles & Human Control

| 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. |

## Target Operating Model

1. Move from broad sprint batching to weekly flow where work-in-progress, blockers, review status, and release readiness stay visible.
2. Consolidate service ownership where practical so the team is not paying coordination tax across dozens of small services.
3. Treat documentation and test evidence as delivery gates, not cleanup tasks after the work is already shipped.
4. Use agents for repeatable delivery work while keeping architecture judgment and product tradeoffs with senior humans.

## Measures to Track

| 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.