AI coding tools have changed how fast software ships. They haven’t changed who runs it. Every extra release, alert and incident still lands on the same SRE, Platform and DevOps teams, and 78% of technology leaders report more incidents since adopting AI-generated code.
So operations teams are turning to agents too. Six in 10 enterprise leaders are piloting, deploying or evaluating automated incident response, according to Caylent. Far fewer of those pilots reach production, and the reason is rarely the model. This guide explains what agentic operations means, what has to sit around an agent before it can work on production systems, and how teams adopt it without giving up control.
Key Takeaways
- Agentic operations means AI agents doing real operations work on production systems: investigating, deciding and acting within limits a team sets, with a record of every step.
- The model is the small part. Identity, permissions, guardrails, evaluation, cost control and shared context decide whether an agent can be trusted in production.
- Teams adopt it a step at a time: read-only first, then pull requests, then approval for each action, and autonomy only for low-risk work.
- The fastest start is one job, such as incident investigation or Kubernetes cost, not a platform project.
What Agentic Operations Means
Agentic operations is the practice of using AI agents to run operations work on production systems. The agents investigate, decide and act within limits a team sets, and every step they take is recorded.
Three things make an operation agentic:
- It works toward a goal across many steps. The agent plans, gathers evidence and adjusts until the job is done or it needs a person.
- It uses tools against live systems. It queries logs, metrics and deploy history, opens pull requests, and, where allowed, makes changes.
- It runs as part of operations. An alert, a schedule or a question in chat starts the work, and the agent sits in the same workflows the team already uses.
An alert fires for a failing checkout service. An agent pulls the recent deploys, matches them against error logs, finds that a config change shrank the database connection pool, and opens a pull request to revert it. Nothing changes until an engineer approves the merge.
What Agentic Operations Is Not
Agentic operations is easiest to define by what it isn’t. It isn’t AIOps, a chat assistant or an agent framework, and it doesn’t mean handing agents unchecked control. Each of those sits next to it, and some feed it:
- A chat assistant. An assistant answers when someone asks. An operations agent is started by events, works through a multi-step task and is accountable for the outcome.
- AIOps under a new name. AIOps correlates signals, cuts noise and recommends next steps, and a person still investigates and acts. Agentic operations picks up where those signals stop.
- An agent framework. Frameworks help teams build agents. Running those agents safely in production is a separate job.
- Autonomy without control. Agents act only within the permissions and approvals a team grants, and they earn more over time.
What an Agentic Operations Platform Needs Underneath
An agentic operations platform provides everything that surrounds the model: identity, permissions, guardrails, run records, evaluation, cost control, shared context and a runtime that never stops. Without it, each team rebuilds those pieces for every agent.
Building an agent that works on a laptop is the easy part. As Komodor CEO Ben Ofiri put it: “It’s easy to spin up one agent, or write one skill. It’s very hard to run a whole fleet of agents on your production systems 24/7.”
The gap is everything around the model. Gartner predicts that over 40% of agentic AI projects will be cancelled by the end of 2027, due to escalating costs, unclear business value or inadequate risk controls. Before an agent can be trusted on production systems, it needs:
- Identity and permissions. Each agent has its own identity and only the access its job needs.
- Guardrails and approvals. Checks that run outside the agent block risky actions, and approval gates hold changes until a person signs off.
- A record of every run. Inputs, tool calls, reasoning, output and cost, so any action can be traced back.
- Evaluation. Graded runs, golden scenarios and shadow tests show whether an agent is good, and whether it stays good after a change.
- Cost control. Cost per run and budgets per agent, so a runaway agent can’t spend the month’s budget in an afternoon.
- Shared memory and context. What one agent learns is reviewed and made available to the next, so no investigation starts from zero.
- A runtime that never sleeps. Agents run around the clock, wherever production runs, on the model the team chooses.
Rebuilding those pieces for every agent is where most pilots stall.
Agentic Operations Use Cases
Teams usually start with operations work that’s frequent, well understood and painful to do by hand. Four jobs come up most often: incident investigation, alert noise, cost, and change risk.
- Incident investigation and root cause. The agent correlates alerts, logs, metrics and recent changes, and returns a root cause with the evidence behind it.
- Alert noise. The agent groups duplicates, separates flapping alerts from real problems, and turns a burst of pages into one incident.
- Kubernetes and cloud cost. The agent finds over-provisioned workloads and idle resources, and proposes rightsizing changes as pull requests.
- Change risk and production readiness. The agent checks releases against the team’s standards before they ship, and flags risky changes before they cause the next incident.
On the Komodor Agentic Operations Platform, these jobs come as ready-to-run agentic workflows across three Solutions: AI SRE, Cost Optimization and AI Software Operations. Digibee, for example, cut its MTTR by 63%. Teams can also build or import their own agents and run them alongside Komodor’s, with the same identity, guardrail and budget controls.
What Changes for SRE and Platform Teams
The job moves from doing the toil to owning the agents that do it. Engineers set the standards agents work to, decide what each one can touch, review the actions that need approval, and improve agents when their runs fall short. Each agent gets an owner and an escalation route, like any production service.
Trust is earned in steps, usually in this order:
| Level | What the agent does | Who decides |
|---|---|---|
| Read-only | Investigates and reports. Nothing changes. | — |
| Pull requests only | Proposes fixes as pull requests | People review and merge |
| Approval per action | Acts once a change is approved | A named approver, each time |
| Autonomous for low-risk work | Runs routine, reversible actions within guardrails, with a full record | The team, by policy |
This is how autonomous operations happens in practice: one low-risk job at a time, with evidence behind every step up.
Common Objections and How Teams Answer Them
The same five concerns come up in almost every evaluation of agentic operations: what happens to agents a team has already built, whether agents can be trusted to act, where data goes, how quality is proven, and what it all costs. Here’s how teams answer each.
“We’re already building our own agents.” Keep building. Building the agent is rarely where projects stall. Running, governing and improving it in production is. The production layer should take the agents a team has already built, in the frameworks it already uses, without a rewrite.
“We don’t trust agents to act in production.” Nobody should on day one. Start read-only or with pull requests only, and grant more as the run records show the agent gets it right.
“Where does our data go?” Teams decide where agents run (their own cloud, on-prem or a managed service) and which model they use. Full data residency means self-hosting the control plane as well as the agents. Secrets should stay out of run records.
“How do we know the agents are any good?” Grade every run, and test each change against golden scenarios and in shadow before it ships. A person decides what goes live.
“This will add to our AI bill.” It shouldn’t go unmeasured. Track what every run costs, cap spend per agent, and move narrow jobs to smaller, cheaper models once the grades show they can handle them.
See how Komodor runs ready-to-run workflows and your own agents side by side, or read the launch announcement.
FAQ
No. AIOps correlates signals, reduces alert noise and recommends next steps, and a person still investigates and acts. Agentic operations uses AI agents that investigate, decide and act within limits a team sets, with a record of every step. Many teams use both: alerts from monitoring and AIOps tools are often what start an agent’s work.
Only when a team chooses. Most start read-only, move to pull requests only, then to approval for each action. Autonomy is usually reserved for low-risk, reversible work, such as restarting a stuck job, and every action stays inside guardrails with a full record. Teams can set a different level for each agent.
It depends more on load than headcount. Teams running meaningful production workloads, often across several clouds or regions, benefit most when change volume grows faster than the team. Teams already writing skills or building agents get value quickly. Earlier-stage teams usually start with one ready-made workflow, such as incident investigation.
A chat assistant answers questions when someone asks. An agent in agentic operations is started by an alert, a schedule or a request, works through a multi-step task using live tools, and is accountable for the result. Chat can still be how engineers talk to their agents, but the agent doesn’t wait to be asked.
Teams adopting agentic operations need four things: signals worth acting on, such as alerts, logs and deploy history the agent can reach; written-down knowledge, like runbooks or a skills repo; a named owner for each agent; and an agreed starting level, usually read-only. A dedicated team to build agent infrastructure isn’t a prerequisite for getting started.
