Software Deliveryapibatch

CI/CD release pipeline

Build, tests, scans and staging run unattended; production ships only on release-manager approval, via ArgoCD.

In short

CI/CD release pipeline is an agent scenario in the Software Delivery sector running on the api, batch channels. Trigger: Merge to main or a v2.14.0 release tag. The Release Supervisor coordinates 4 specialist agents (Build Agent, Security Agent, Tester Agent, Release Agent) across 4 systems (CI Runner (GitHub Actions), SonarQube + Snyk, Kubernetes / ArgoCD, Slack); of 10 steps, 2 write to systems and 3 are checks or approvals. Outcome: v2.14.0 in production, health checks green, release notes published, rollback plan ready. Approval rule: Every production deploy → release-manager approval mandatory; a critical CVE closes the gate automatically.

Software Delivery

CI/CD release pipeline

run #41,289,685elapsed 0 mstokens 0est. cost $0.000status —Approval rule: Every production deploy → release-manager approval mandatory; a critical CVE closes the gate automatically

Agents in this run

Release Supervisorsupervisorplans, routes, approves

Build Agentaginies-sunkar-1 · on-premBuilds, runs unit/integration tests, pushes the image

Security Agentaginies-tulpar-4 · on-premEvaluates SAST and dependency scans, gates by policy

Tester Agentaginies-tulpar-4 · on-premRuns smoke tests on staging, compares results with the baseline

Release Agentaginies-talay-2 · on-premDeploys via ArgoCD, writes release notes, informs the team

Audit trail00:00.00

Tag v2.14.0 on main: 23 commits, 4 PRs, 6 days since the last release
CI: build 4 min 12 s, 1,284 unit + 96 integration tests passed, image pushed
SAST: 0 critical, 2 medium; dependencies: 1 high CVE (lodash 4.17.20)
High CVE sits on an unreachable code path → policy: pass, follow-up PROJ-4877 raised
Plan: staging deploy → smoke tests → release-manager approval → production → notes
Deployed to staging (argocd sync billing-api-staging), 3/3 pods ready
Smoke tests 42/42 passed; p95 latency 184 ms, 3% better than baseline
Production deploy → release manager approved change record CHG-2107
Production rollout canary 10% → 100%, health checks green, rollback ready
Release notes published; summary and 4 PR links posted to #releases
■completed

Simulation · derived from real agent definitions · every agent can be built by dialogue with the Autonomous Agent and validated with a test corpus

Trigger

Merge to main or a v2.14.0 release tag

Outcome

v2.14.0 in production, health checks green, release notes published, rollback plan ready

Agents

SUPERVISOR

Release Supervisor

Decomposes the objective, delegates to agents, manages approval points, merges the result.

aginies-sunkar-1 · on-prem

Build Agent

Builds, runs unit/integration tests, pushes the image

run_buildrun_testspush_image

aginies-tulpar-4 · on-prem

Security Agent

Evaluates SAST and dependency scans, gates by policy

run_sastscan_dependenciestriage_findings

aginies-tulpar-4 · on-prem

Tester Agent

Runs smoke tests on staging, compares results with the baseline

run_smoke_testscheck_healthcompare_baseline

aginies-talay-2 · on-prem

Release Agent

Deploys via ArgoCD, writes release notes, informs the team

deploy_argocddraft_release_notespost_slack

Steps

#	Kind	Agent	What happens	System	ms	tok
01	ingest	Release Supervisor	Tag v2.14.0 on main: 23 commits, 4 PRs, 6 days since the last release	—	180	—
02	tool	Build Agent	CI: build 4 min 12 s, 1,284 unit + 96 integration tests passed, image pushed	CI Runner (GitHub Actions)	3,600	—
03	tool	Security Agent	SAST: 0 critical, 2 medium; dependencies: 1 high CVE (lodash 4.17.20)	SonarQube + Snyk	2,400	—
04	verify	Security Agent	High CVE sits on an unreachable code path → policy: pass, follow-up PROJ-4877 raised	—	900	1,100
05	plan	Release Supervisor	Plan: staging deploy → smoke tests → release-manager approval → production → notes	—	280	200
06	write	Release Agent	Deployed to staging (argocd sync billing-api-staging), 3/3 pods ready	Kubernetes / ArgoCD	1,800	—
07	verify	Tester Agent	Smoke tests 42/42 passed; p95 latency 184 ms, 3% better than baseline	—	2,600	600
08	approval	Release Supervisor	Production deploy → release manager approved change record CHG-2107	—	4,000	—
09	write	Release Agent	Production rollout canary 10% → 100%, health checks green, rollback ready	Kubernetes / ArgoCD	3,400	—
10	notify	Release Agent	Release notes published; summary and 4 PR links posted to #releases	Slack	320	700

Simulation metrics (estimated)

950runs / month

94.7%success

18.9 sp50

$0.0075est. cost / run

Approval rule

Every production deploy → release-manager approval mandatory; a critical CVE closes the gate automatically

Step profile

ingest · 1plan · 1tool · 2verify · 2approval · 1write · 2notify · 1

SYSTEMS

CI Runner (GitHub Actions)api
SonarQube + Snykapi
Kubernetes / ArgoCDapi
Slackchannel

What you need to run it

Credentials and a test environment for the enterprise APIs involved
Channel integration (WhatsApp Business API, SMS gateway or in-app widget)
An authorised role and approval channel for the approval rule (mobile approval card, e-mail or ticket)
SSO with the organisation’s identity provider and a scenario-specific authorisation group

Global pattern

Mirrors agent-augmented release pipelines at large software teams practising GitOps and progressive delivery

Packages that include this scenario SDLC agent team → Sector: Software Delivery →

Related agents

Software DeliveryHITL

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.

1,900 runs / month 90.4% success 5 agents

Software DeliveryHITL

Feature delivery pipeline

A Jira epic turns into criteria, design, code and tests via agents; humans only review and approve the PR.

1,200 runs / month 91.2% success 5 agents

Software DeliveryHITL

Incident response and hotfix

Alert fires, bug reproduced, patch and regression run ready; on-call approves, post-mortem already drafted.

320 runs / month 90.5% success 5 agents

Time to move from experimenting with AI to transforming with it.

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Book a 30-minute discovery session → dahi@aginies.com

Your 2–3 priority problems
Live demo of a similar scenario
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