PM, analyst, architect, developer and test agents: the whole line from feature request to production.
In software teams the bottleneck is usually the rest of the line: requirement clarification, design decisions, review, release, incident response. Aginies runs that line with an SDLC agent team connected to Jira, Git and CI/CD; Code Aginies provides completion inside the IDE with the organisation’s own models. Production decisions and architectural approval always stay with people.
In Software Delivery, Aginies offers 8 live agent scenarios and 3 solution packages: Legacy module modernisation: analysis, tests, rewrite, PR, Feature delivery pipeline, CI/CD release pipeline, Incident response and hotfix, Code review and security gate, Backlog grooming and estimation, Staged OTA update rollout; solutions: SDLC agent team, IoT device fleet operations, Agent outsourcing (agent workforce). The blockers on the way to production are Requirement ambiguity, Review bottleneck, Release risk, Code leakage concern; on the compliance side 3 requirements (including Code and completion models inside the organisation; Code Aginies on-prem) are part of the deployment.
Requirement ambiguity
Three meetings before a request becomes a story; acceptance criteria written afterwards.
Review bottleneck
PRs wait for days; security scanning is a separate queue.
Release risk
Release notes, scope and rollback plans by hand; every release is a late night.
Code leakage concern
Cloud code assistants carry the organisation’s source code outside.
Three starting points; each measurable in a short pilot on existing systems.
- 01agent scenarioFeature delivery pipeline →
The requirement-to-PR line is the most visible bottleneck; a 4–6-week pilot with one team.
- 02agent scenarioCode review and security gate →
A security and quality gate on every PR is low-risk and measured immediately.
- 03agent scenarioLegacy module modernisation: analysis, tests, rewrite, PR →
Modernisation programmes run for years; characterisation tests make the steps safe.
Agents you can watch live for this sector
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.
Backlog grooming and estimation
Incoming requests are triaged, clarified and sized; a sprint proposal goes to the product owner for approval.
Staged OTA update rollout
Firmware rolls out in 1% → 10% → 100% stages; a health gate at every stage, human approval for critical fleets.
Derived from the 8 scenarios in the sector: supervisors, specialist agents, Hub model families and systems touched.
Summed from the simulation definitions of the sector scenarios; recalculated for your scale in the discovery session.
- Code and completion models inside the organisation; Code Aginies on-prem
- Human approval on production deployment and architectural decisions
- SAST, dependency and secret scanning on every PR; evidence stored with the release
- Source code and intellectual property: code never leaves the organisation; Code Aginies and completion models on-prem
- Secure development requirements (incl. regulator expectations in regulated sectors): SAST, dependency and secret scanning on every PR, evidence stored per release
- Production deployment and architecture decisions with people; the agent writes only in assigned repositories and branches
Guardrails, approval and the evidence ledger: the compatible product Thulpar ↗
Code Aginies and coding models in the organisation’s data centre or in our data centre in Türkiye through the closed-circuit deployment; SDLC agents connected to Jira, Git and CI/CD with service accounts.
Approval rulesHuman-approval thresholds of the sector scenarios.
- 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
- Backlog grooming and estimationSprint scope → product owner approval; agents only propose, never commit items to a sprint
- iOS feature implementation (hired developer agent)Every PR is reviewed by a person; production deployment under the team lead’s approval
- Regression test cycle (hired QA agent)The release “go / no-go” decision stays with the release manager
| 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.
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