Picture two enterprise teams working on the same problem: a legacy app that needs a platform upgrade, security fixes, and a cloud-ready release path.
Team A uses AI like a faster autocomplete. Helpful, but the work still happens in long bursts: someone writes an upgrade plan, someone else makes the changes, then the build breaks, and releases slip.
Team B treats AI like a junior collaborator inside the delivery system. An agent proposes an upgrade plan, applies changes, runs builds/tests, opens a pull request, and leaves a summary for review—while humans stay responsible for architecture, approvals, and release decisions.
That difference is not “more AI.” It’s DevOps discipline applied to a new reality: AI that can take actions, not just answer questions.
Microsoft and GitHub have been moving in this direction with Copilot “agent mode” and coding agents designed to work through issues and pull requests, run modernization steps, and produce reviewable outputs.
This post breaks down what agentic AI is, why it’s a DevOps topic, and how to adopt it in a way that improves delivery without creating a governance mess.
What “agentic AI” means (and why DevOps teams should care)
Most people think “generative AI” equals chat: you ask, it answers.
Agentic AI is different. The point isn’t a better paragraph. The point is a system that can:
- Work toward a goal over multiple steps
- Use tools (repos, CI, pipelines, ticketing)
- Create artifacts (PRs, upgrade plans, scripts)
- Ask for review when it’s done, like a teammate
Microsoft’s own guidance around Copilot-driven modernization describes agents completing modernization tasks in the background, producing PRs and summaries that developers review.
That’s why this lands squarely in DevOps: once something can change code or infrastructure, you need the same controls you’d apply to any contributor—identity, permissions, review gates, traceability, and release safety.
Where agentic AI shows up in the Microsoft ecosystem today
1) GitHub Copilot agents for modernization work
Microsoft’s .NET team published a step-by-step guide focused on modernizing .NET using GitHub Copilot agent mode: generate an upgrade plan, apply changes, fix build issues, validate tests, and produce a report for review.
There’s also Microsoft Learn guidance describing Copilot coding agents modernizing apps by working through delegated tasks and producing PRs.
Practical takeaway: upgrades and migrations become a plan → apply → validate → review loop that fits naturally into a PR-based delivery model.
2) “Control plane” thinking for agents
At Ignite, Microsoft talked about managing and securing agents with centralized control concepts (positioned as a way to run human-led, agent-operated work safely).
You don’t need the marketing language to take the lesson: if agents multiply, governance must scale.
3) Data and privacy boundaries still matter
Microsoft’s guidance for Copilot in Microsoft 365 emphasizes keeping prompts, retrieved data, and outputs within the service boundary and processing via Azure OpenAI (not consumer OpenAI services).
For GitHub Copilot specifically, GitHub documents governance-oriented positioning for Copilot Business and explains Copilot’s data processing at a high level.
Practical takeaway: treat “what data the agent can see” as an architectural decision, not a default.
The DevOps shift: AI becomes a contributor, not a tool
If an agent can open PRs or propose infrastructure changes, DevOps teams should treat it like any other non-human actor:
- A distinct identity
- Least-privilege access
- Audit trails
- Approval gates
- Safe rollout patterns
This is the difference between “AI helped me write code” and “AI changed my system.”
Three high-value use cases (that don’t require a moonshot)
Use case 1: Modernization PRs (framework upgrades, dependency refresh, security fixes)
This is the cleanest entry point because it produces a reviewable artifact: a PR.
What the agent does (typical):
- Analyze repo + target framework/runtime
- Draft an upgrade plan
- Apply transformations
- Run build/tests
- Document what changed and what still needs humans
Microsoft’s .NET modernization guidance is explicitly oriented around this workflow.
Where humans stay essential: architectural decisions, breaking change handling, release planning, and acceptance criteria.
Use case 2: Infrastructure-as-code assistance (scripts, pipelines, environment bootstrapping)
Many teams already use copilots to draft Terraform/Bicep, YAML pipelines, and runbook scripts.
The agentic twist is that it can produce the change as a PR, then iterate until checks pass—if your pipeline is set up well.
Where humans stay essential: networking, identity, secret boundaries, and production safety.
Use case 3: “Ops hygiene” work (runbooks, alerts, post-incident follow-ups)
Agentic workflows can help create or update:
- Runbooks that match what the team actually does
- Post-incident action items turned into PRs (documentation, checks, guardrails)
This only works if you already have a DevOps habit of writing down operational knowledge and keeping it current.
What practitioners warn about (and why it’s useful)
In DevOps communities, two consistent warnings show up:
- Over-dependence without understanding (teams fear losing fundamentals)
- Agent sprawl (too many AI entry points, unclear ownership, unclear controls) — the governance concern, not the AI concern
You don’t need to agree with every comment to extract the pattern: the risk isn’t “AI exists.” The risk is unowned automation.
A practical rollout model for agentic AI in DevOps
Step 1: Choose one workflow with clean boundaries
Good pilot candidates:
- Upgrade one service to a newer runtime
- Refresh dependencies for one repo
- Add test coverage for a known brittle module
Avoid first:
- Broad refactors across many repos
- Production incident automation that can trigger changes without review
Step 2: Define what the agent can access
Write it down:
- Repos: which ones, and why
- Environments: dev only at first
- Data: what it can and cannot see (especially logs, tickets, customer data)
Step 3: Make “review” non-negotiable
If the agent opens PRs, use the same rules you’d use for a new engineer:
- Required reviewers
- Branch protections
- Mandatory CI checks
- Security scanning gates
Step 4: Create a failure-safe path
Plan for:
- Partial PRs that don’t compile
- Changes that pass tests but violate conventions
- Hidden breaking changes
The point of DevOps is not “never fail.” It’s “recover predictably.”
Step 5: Instrument outcomes
Use delivery metrics as guardrails, not vanity.
DORA’s guidance defines common delivery performance measures like deployment frequency, change lead time, change fail rate, and recovery time.
Track them at the service level, not as a leaderboard.
Step 6: Scale with governance, not enthusiasm
Once the pilot works:
- Expand to a second repo
- Standardize prompts/patterns
- Add a lightweight “agent registry” (who owns it, what it touches, how it’s monitored)
Implementation checklist (copy/paste)
Use this as your “we can do this safely” baseline:
- Define one pilot repo and one target outcome (upgrade, deps refresh, tests, pipeline change).
- Enable branch protections and required reviews before agents create PRs.
- Confirm CI checks are meaningful (tests, lint, security scan).
- Use least-privilege permissions for the agent identity.
- Restrict secrets access; never allow plaintext secrets in prompts or files.
- Add a clear PR template for agent-generated changes (goal, approach, test evidence, known risks).
- Require the agent to run builds/tests and attach results where possible.
- Define “stop conditions” (what triggers human takeover).
- Log and audit agent actions (PRs opened, files changed, pipelines triggered).
- Decide how you’ll measure success (see next section).
- Document what data the agent can access and what it must not touch.
- Run a post-pilot review and update the playbook.
Success metrics that make sense (without chasing vanity)
Start simple. A useful scorecard includes:
- Delivery flow: change lead time trends (service-level)
- Release safety: change fail rate and recovery time trends
- Engineering productivity signals: fewer repeated “upgrade toil” tasks, fewer manual fix loops
- Quality signals: test pass rate stability, reduced regression patterns
- Governance signals: % agent PRs that pass review without policy exceptions, audit completeness
If metrics get gamed, they stop being metrics. Keep them tied to outcomes and learning.
Region angle: ANZ, US, and Canada considerations
Agentic AI adoption looks similar across regions, but the risk questions change based on how you handle data and accountability.
- ANZ (Australia): The Privacy Act and Australian Privacy Principles are a common reference point when personal information is involved.
- Canada: PIPEDA is the federal private-sector privacy law for personal information in commercial activities.
- US: Many organizations operate across a patchwork of federal sector rules and state privacy laws, which often pushes teams toward stronger governance-by-default patterns.
This is not legal advice. The practical DevOps takeaway is consistent: control what agents can access, log what they do, and keep humans accountable for approvals.
Suggested feature image (simple, minimal, no logos)
A clean diagram-style visual:
Title: “Agentic AI in the DevOps Loop”
Visual: A circular flow: Plan → Code → Build → Test → Release → Monitor
Overlay a “PR gate” box between Code and Build with labels: “Agent proposes PR” / “Human reviews” / “CI checks”
Alt text: “DevOps workflow with agentic AI proposing pull requests and humans approving changes.”
Run a safe pilot, not a big-bang program
If you want to test agentic AI without turning it into a platform rewrite:
- Pick one modernization target (one repo, one outcome).
- Put it behind your existing DevOps gates.
- Measure outcomes with delivery and stability metrics.
Osmosys can run an “Agentic DevOps Pilot” workshop: a short assessment of your current delivery setup (repos, CI/CD, environments, controls), followed by a guided pilot where an agent produces reviewable PRs for a defined modernization task.
Internal reading (recommended):
- Agile and DevOps Delivery for Microsoft Projects (Osmosys)
- AI Governance Checklist for Dynamics 365 + Power Platform (Osmosys)
- Trust by Design: Data Governance for Cloud + AI (Osmosys)
FAQs
What does DevOps look like when AI agents can write code?
DevOps stays the same in principle—version control, CI checks, reviews, controlled releases—but you treat the agent like a contributor with identity, permissions, and audit trails.
Is agentic AI the same as ChatGPT?
No. Chat-style tools respond to prompts. Agentic AI can execute multi-step work toward a goal, using tools like repos and CI to produce artifacts such as pull requests.
Where should teams start with agentic AI in DevOps?
Start with PR-based modernization tasks (dependency updates, framework upgrades, test additions). These create reviewable changes and fit naturally into existing delivery controls.
What’s the biggest risk with agentic AI adoption?
Unowned automation: agents with unclear access, unclear approvals, and no monitoring. The fix is governance-by-default—least privilege, review gates, and audit logs.