SDLC agent team
PM, analyst, architect, developer and tester agents working together in the CI/CD pipeline.
SDLC agent team is an Aginies solution package for the Software Delivery sector in the “software development & technology productivity” area. What the agents do: The Project Manager Agent triages requests, the Analyst Agent writes acceptance criteria, the Architect Agent produces the design and impact; the Developer Agent implements on a branch; the Tester Agent generates and runs unit and integration tests and reports coverage. Expected outcomes: lead time from request to production drops from days to hours; standard, measurable quality: coverage, security and regression on every PR; engineers focus on direction and approval; agents take the repetitive work. The package uses the Supervisor · sub-agents, Tools / MCP (Git, CI, Jira), Human-in-the-loop, Stateful execution capabilities of the Aginies platform and can be watched live through the Legacy module modernisation: analysis, tests, rewrite, PR, Feature delivery pipeline, CI/CD release pipeline, Incident response and hotfix, Code review and security gate scenarios in the agent library.
Software teams lose time in hand-offs between request, analysis, design, coding, testing and deployment. Code-generating assistants exist; a team that owns the process from ticket to production with defined approval points does not.
- 01The Project Manager Agent triages requests, the Analyst Agent writes acceptance criteria, the Architect Agent produces the design and impact.
- 02The Developer Agent implements on a branch; the Tester Agent generates and runs unit and integration tests and reports coverage.
- 03SAST, dependency scanning and staging smoke tests run automatically in CI/CD; production deployment goes to release-manager approval.
- 04Every step is traced in Jira, Git and CI; code review and the production decision always stay with people.
- PM, analyst, architect, developer and test agents on Jira, Git and CI/CD
- From requirement to PR: analysis note, design decision, code, tests, review
- In-IDE completion with Code Aginies; code never leaves the organisation
- Incident response and hotfix lane; release notes
- Service accounts and permission model for Jira, Git and CI/CD
- Coding standards, review rules and definition of done
- Scope boundaries: which repositories, which environments
Derived from the linked scenarios: the supervisors, specialist agents, models and systems that run this package.
Summed from the simulation definitions of the linked scenarios; recalculated for your scale in the discovery session.
- Lead time from request to production drops from days to hours
- Standard, measurable quality: coverage, security and regression on every PR
- Engineers focus on direction and approval; agents take the repetitive work
| PRs accepted on first review | 60–75% |
| Requirement-to-PR lead time | 40–50% shorter |
| Test coverage | +15–25 points |
Target ranges from comparable deployments; baseline and target are fixed together in the first week of the pilot.
Scenario-level approval rules plus the package’s general controls.
- Legacy module modernisation: analysis, tests, rewrite, PRProduction schema change or out-of-scope file → architect approval; PR merge always human
- Feature delivery pipelineEvery PR → at least 1 human code review approval; agents never push to main directly
- CI/CD release pipelineEvery production deploy → release-manager approval mandatory; a critical CVE closes the gate automatically
- Incident response and hotfixEvery emergency production deploy → on-call engineer approval; the hotfix PR still gets a human review
- Code review and security gateEvery PR → human CODEOWNERS approval; a high SAST finding or coverage < 80% blocks the gate
- packagePR merge and production deployment always under human approval
- packageThe agent writes only in assigned repositories and branches; secret scanner on every PR
- 1 · Pipeline integration · 2 weeksJira, Git, CI/CD or device management connected; permission model and scope boundaries written.
- 2 · Pilot · 4–6 weeksOne team or one device segment; every output passes human review, acceptance rate measured.
- 3 · ScaleRemaining teams and segments; automated steps widen, production and architecture decisions stay with people.
This package’s scenarios in the agent library
Legacy module modernisation: analysis, tests, rewrite, PR
Starts from a Jira epic; the old module is read, its behaviour pinned with tests, rewritten and opened as a PR with evidence.
Feature delivery pipeline
A Jira epic turns into criteria, design, code and tests via agents; humans only review and approve the PR.
CI/CD release pipeline
Build, tests, scans and staging run unattended; production ships only on release-manager approval, via ArgoCD.
Incident response and hotfix
Alert fires, bug reproduced, patch and regression run ready; on-call approves, post-mortem already drafted.
Code review and security gate
Agents review, scan and coverage-check every PR; fixes land first, a human approves only clean PRs.
Time to move from experimenting with AI to transforming with it.
In a 30-minute discovery session we take your 2–3 priority business problems, show a live demo of a similar scenario, and draft a roadmap that starts with the Value layer.
- Your 2–3 priority problems
- Live demo of a similar scenario
- Roadmap starting with value analysis