Sequence Diagrams for Autonomous Code Agents Interacting with CI/CD

Stop unsafe CI/CD edits by autonomous agents — sequence diagrams that enforce safe request, patch, and verification flows

Autonomous coding agents like Claude Code and Cowork accelerate developer productivity, but they also introduce new risks when interacting with CI/CD pipelines. If an agent can push directly to main, modify deployment manifests, or bypass scans, you quickly trade speed for outages and supply-chain vulnerabilities. This article gives you production-ready sequence diagram patterns (with PlantUML snippets and notation standards) that show exactly how agents should request, patch, and verify changes in CI/CD safely — with minimal friction and clear auditability.

Why this matters in 2026: trends and the current threat surface

Late 2025 and early 2026 saw rapid enterprise adoption of developer-grade autonomous assistants. Anthropic's rollout of Cowork and enhancements to Claude Code moved agent capabilities from research labs into desktop and repo workflows, giving agents filesystem and repository access. That convenience has created real operational questions:

• How should agents authenticate to CI/CD systems without long-lived credentials?
• When is human review required versus automated policy enforcement?
• How do we ensure reproducible verification artifacts (builds, SBOMs, tests) for audit and rollback?

Regulatory and security communities responded in 2024–2025 with stronger recommendations: ephemeral credentials (OIDC), policy-as-code (OPA/Rego), and signed provenance for build artifacts. In 2026 these practices are de facto expectations for any CI/CD flow that allows agent-initiated changes.

High-level safe patterns (inverted pyramid)

In most environments the safest and most practical pattern is:

1. Agents propose changes on a feature branch and create a pull request (PR).
2. CI runs full verification including tests, SCA, SAST, SBOM, and reproducible builds.
3. Policy engine evaluates the PR; only allow automated merge if policies are met or an explicit human approval token is provided.
4. Deployment is gated by feature flags and canary rollouts; observability detects regressions for automatic rollback.

Actors and notational standards for the diagrams

• Agent — autonomous code agent (Claude Code / Cowork) executing a task.
• Dev — human developer or owner who can approve sensitive changes.
• Repo — Git repository hosting the code (branch/PR model).
• CI — CI/CD system (GitHub Actions, GitLab CI, Jenkins, etc.).
• Scanner — SCA/SAST tool (Dependabot, Snyk, Semgrep).
• Policy Engine — enforces policy-as-code (OPA/Rego, Conftest).
• Artifact Store — container registry or artifact repository (signed images, SBOM).
• Gate — human approval or automated merge policy.

Sequence diagram #1 — Safe propose-and-verify (recommended default)

Pattern: Agent creates branch & PR; CI verifies; policy verifies; merge only on pass or explicit approval. This is the default safe pattern for production repositories.

@startuml
actor Agent
actor Dev
participant Repo
participant CI
participant Scanner
participant Policy
participant ArtifactStore
participant Gate

Agent -> Repo: create branch + commit (no direct push to main)
Agent -> Repo: open Pull Request (with rationale + tests run locally)
Repo -> CI: trigger CI pipeline on PR
CI -> CI: run unit tests
CI -> Scanner: run SAST/SCA (generate SBOM)
Scanner -> CI: send scan results
CI -> ArtifactStore: build artifact + generate checksum + sign (if build passes)
CI -> Policy: submit results + SBOM + signatures
Policy -> CI: policy verdict (pass/warn/fail)
CI -> Repo: set status checks
Repo -> Gate: if policy pass && checks pass -> allow merge
Gate -> Repo: if manual approval required -> notify Dev
Dev -> Repo: review + approve
Repo -> CI: merge PR into main
CI -> ArtifactStore: tag release artifact + deploy with canary
ArtifactStore -> CI: deployment response
CI -> Monitor: start observability checks
Monitor -> CI: if regression -> trigger rollback
@enduml
  

Key notation rules used above: use explicit actor labels, keep message verbs concise (create, trigger, run, submit), and attach artifacts (SBOM, signatures) to messages where provenance matters. For diagrams.us template standards, include a legend and versioning on the diagram file: e.g., v1.0 — 2026-01-17.

Security and safety annotations for diagram #1

• Agent must use ephemeral credentials (OIDC or short-lived PAT) scoped to create branches and PRs only.
• Build artifacts must be signed and accompanied by an SBOM and provenance metadata.
• Policy engine enforces runtime and supply-chain rules before merge.
• All events logged to an immutable audit store with signed timestamps.

Sequence diagram #2 — Trusted-agent accelerated path (restricted)

If your organization has rigorously validated an agent (e.g., dedicated workspace, reproducible tests, signed commits), you may allow an accelerated path for low-risk changes. This should be limited to non-production branches or specific modules.

@startuml
actor Agent (trusted)
participant Repo
participant CI
participant Policy
participant ArtifactStore
participant Gate

Agent (trusted) -> Repo: push branch with signed commit
Repo -> CI: trigger lightweight CI (lint + unit tests)
CI -> Policy: submit lightweight results
Policy -> CI: if thresholds met -> auto-merge
CI -> ArtifactStore: build + tag (staging)
ArtifactStore -> CI: deploy to staging with canary
CI -> Monitor: run integration smoke tests
Monitor -> CI: promote to prod only on success and validation
@enduml
  

In this pattern, strong verification happens before promotion to production, and signed commits + provenance allow retrospective audits. Only allow this flow for well-audited, minimal-impact changes.

Sequence diagram #3 — Emergency patch with human-in-loop rollback

When an urgent fix is needed (hotfix), follow a tightly controlled flow that requires high-assurance verification, explicit Dev approval, and immediate monitoring with automatic rollback triggers.

@startuml
actor Agent
actor Dev
participant Repo
participant CI
participant Scanner
participant Policy
participant ArtifactStore
participant Monitor
participant Rollback

Agent -> Repo: create hotfix branch + commit (flagged)
Agent -> Repo: open hotfix PR and ping on-call
Repo -> CI: run exhaustive pipeline (tests, SCA, fuzzing)
CI -> Scanner: run extended security scans
Scanner -> CI: results
CI -> Policy: require on-call approval token
Dev -> Repo: approve with one-time token
Repo -> CI: merge to main
CI -> ArtifactStore: deploy to prod immediately with feature flag off
CI -> Monitor: run immediate health checks
Monitor -> Rollback: if anomaly -> revert commit + redeploy previous artifact
Rollback -> Repo: create revert commit
@enduml
  

Practical controls and policies to implement

• Least privilege: Agent tokens limited to branch/PR scopes and ephemeral OIDC flows.
• Signed commits and commits + PR provenance: Require GPG or commit signing from agent runtime.
• Policy-as-code: Use OPA/Rego or Conftest to evaluate SBOMs, test coverage, license checks before merge.
• Immutable audit logs: Append-only store for all agent actions, signed with organizational keys.
• Observability gates: Canary + feature-flag rollout with automated rollback thresholds.
• Testing thresholds: Block merges if unit/integration/end-to-end coverage drops below X% (your org threshold).

Designing diagrams for teams: notation and template standards

Teams need consistent diagram standards so that sequence diagrams are machine-readable, reusable, and auditable. Adopt these rules:

• Use PlantUML for versionable, text-based diagrams. Include a header with team, repo, and date.
• Label every message with the action and artifact (e.g., "run SAST -> SBOM v1.2").
• Color-code or annotate messages that require human approval or that carry credentials.
• Include a legend and security annotations section beneath the diagram.
• Store diagrams alongside code (docs/diagrams/) and automate rendering in CI to keep them in sync.

Verification artifacts and their place in the sequence

Successful verification is not just "tests passed" — it is a bundle of provable artifacts. The sequence must show production of these artifacts and their consumption by the policy engine:

• SBOM — declared component list for the build.
• Signed build artifact — image/jar with signature and checksum.
• Test report — unit/integration/e2e results with coverage metrics.
• SAST/SCA report — findings with severity and resolved state.
• Provenance metadata — builder identity, exact commit, environment hash.

Actionable checklist for implementing the diagrams

1. Enforce branch+PR model: disable direct pushes to main.
2. Require tokenized agent identity: OIDC or ephemeral PAT per agent workspace.
3. Integrate policy-as-code in CI and run it as an explicit step with a pass/fail status check.
4. Sign artifacts and record SBOM; store in Artifact Store with immutable tags.
5. Set up automated canary rollouts and pre-merge staging promotions.
6. Log all agent actions to an immutable audit ledger and rotate keys periodically.
7. Render sequence diagrams from PlantUML files in docs and include them in PR template checklists.

Real-world example: Anthropic Cowork and desktop agent access

Anthropic's Cowork (research preview) lowered the barrier for agents to access local files. In a corporate setting, this means agents running on employee machines may access repo clones or local credentials. For these scenarios follow stricter controls:

• Restrict Cowork/Claude instances to sandboxed workspaces that cannot read SSH keys or long-lived credentials.
• Issue ephemeral tokens from a centrally managed broker for each session.
• Always force a PR-based workflow; never allow both local agent and remote CI to push directly to main without reconciliation and signed provenance.

"The convenience of local autonomous agents demands stronger supply-chain controls — not weaker ones."

Testing and verification playbooks

Include explicit test steps in the sequence diagrams and CI pipelines. Example minimal verification playbook for agent PRs:

1. Pre-commit hooks run in agent workspace (format, lint).
2. CI: unit tests (fail fast).
3. CI: dependency SCA and license checks.
4. CI: reproducible build + artifact signing.
5. CI: run integration tests in ephemeral environments.
6. CI: record SBOM and submit to Policy Engine.
7. Policy Engine: evaluate and attach policy status to PR.

Future predictions (2026 and beyond)

Expect three converging trends through 2026:

• Agent capability manifests: a signed descriptor that enumerates what an agent attempted and was allowed to do. These will become standard in supply-chain audits.
• Runtime enforcement: cloud CI/CD platforms will natively enforce agent scopes and ephemeral credentials with built-in policy engines.
• Provenance-first pipelines: Every artifact will require a provenance chain (commit -> build -> test -> sign) before promotion. This will be a compliance baseline.

Annotated PlantUML template (downloadable standard)

Use the following as the base template for any agent-CI sequence diagram. Keep it in docs/diagrams/agent-ci.puml and render during doc builds.

@startuml agent-ci v1.0 - diagrams.us
' Actors
actor Agent as A
actor Developer as D
participant Repo as R
participant CI as C
participant Scanner as S
participant Policy as P
participant Artifact as AR

' Flow
A -> R : create branch + commit (signed)
A -> R : open PR (include agent-manifest)
R -> C : trigger pipeline(PR)
C -> S : run SAST + SCA -> SBOM
S -> C : findings (annotate)
C -> AR : build + sign artifact
C -> P : submit SBOM + testReports + signatures
P -> C : policy verdict
C -> R : set checks
R -> D : request approval (if required)
D -> R : approve (with one-time-token)
R -> C : merge -> deploy with canary
@enduml
  

Actionable takeaways

• Always require a PR-based flow for agent-initiated changes unless tightly controlled and audited.
• Use ephemeral credentials and agent manifests to create an auditable trust boundary.
• Enforce policy-as-code in CI and require signed artifacts + SBOM for merges and deployments.
• Render and store sequence diagrams alongside code to keep operational intent synchronized with implementation.

Conclusion & call-to-action

Autonomous agents like Claude Code and Cowork are changing how software is created — but they must interact with CI/CD under strict, auditable rules. Use the sequence diagram patterns above as a starting point: adopt the PR-first model, require signed artifacts and SBOMs, enforce policy-as-code, and ensure every automated decision is logged and reviewable. These sequence diagrams are not just diagrams — they are operational contracts.

Next step: Download our PlantUML template pack and ready-to-use CI policy snippets for GitHub Actions and GitLab CI at diagrams.us/templates/agent-ci. Integrate the template into your repo's docs/diagrams folder and automate rendering in your documentation pipeline. If you want an audit-ready review, contact our team for a diagram-driven policy review tailored to your stack.

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