Reasoning Agent
Observes signals across all connected tools in real time. Correlates them across sources. Performs root cause analysis with evidence. Decides which Method of Procedure to run.
In Opstral: Sentinel AI.
What Is Agentic AIOps?
Category Definition
Agentic AIOps decomposes operational work across a system of autonomous AI agents that reason, act, and validate together. It is the practical architecture for autonomous IT operations once the work is too large for any single model to hold.
The definition
Agentic AIOps is the application of multi-agent AI systems to IT operations. The "agentic" part means each agent is autonomous within its scope: it perceives its inputs, decides on its outputs, and acts on the world (or its peer agents) without a human stepping into the loop for routine decisions. The "AIOps" part is the operations domain: signals, incidents, runbooks, change management, and the resolution work that has historically been done by humans pulling data from dozens of tools.
The term has surfaced in 2024 and 2025 as a way to distinguish products that genuinely use the autonomous-agent architecture from products that put an ML model in front of an alerting console and call themselves AI-powered. The architectural difference is real. The category claim should rise or fall on it.
Why "AI-powered AIOps" is not the same thing
Most AIOps platforms apply machine learning to one slice of the operational pipeline. The most common slice is anomaly detection: an ML model flags a metric that deviates from baseline. The second most common is alert correlation: a model groups related alerts into a single incident. Both are useful, both are real applications of AI, and neither is agentic.
The output of those models is still an alert (or a smarter alert) that a human picks up. There is no agent that decides what to do. There is no agent that acts. There is no agent that verifies the outcome. The autonomy stops at the dashboard.
Agentic AIOps reorganizes the platform around agents that do those things. The decomposition is intentional. Each agent has scope, policy, audit, and the capability to act on its own outputs.
The three roles in an agentic AIOps system
The pattern that has emerged in production deployments decomposes the work into three agents.
Execution Agent
Receives a structured Action Ticket from the reasoning agent. Runs the MOP with pre-checks, dependency management, and validation. Rolls back automatically if validation fails. Holds the integration credentials. Acts under the customer's policy.
In Opstral: ProcBot.
Validation Agent
Confirms the fix actually resolved the underlying issue (not just superficially closed the ticket). Monitors for recurrence. Scores MOP effectiveness across executions. Feeds learnings back into the reasoning agent's library.
In Opstral: Sherlock.
The agents communicate through structured artifacts: signal records flow into the reasoning agent, Action Tickets flow to the execution agent, outcome records flow to the validation agent, and learning records flow back to the reasoning agent. Every artifact is recorded immutably. The audit trail is the agent communication log.
Why decompose the work this way
The reasonable question is why three agents instead of one large model that does everything. Three reasons.
Separation of concerns produces clearer audit trails. When the regulator (or your own SRE lead) asks "who decided to execute this change, and who validated it," the answer should be unambiguous. With a decomposed system the answer is "the reasoning agent decided based on this signal correlation, the execution agent ran this specific MOP with these inputs, the validation agent confirmed this outcome." With a monolithic model the answer is some flavor of "the AI decided." That distinction matters in any environment that does change management.
Governance becomes per-agent. The reasoning agent reads broadly. The execution agent writes narrowly. Policy can be tighter on the execution surface (which MOPs can run, in which environments, under which conditions) while still letting the reasoning agent observe everything. A monolithic model has one privilege level, which is either too broad or too narrow for production change.
Reliability and replaceability improve. Each agent can fail, restart, be replaced, or be improved independently. The execution agent can be updated without retraining the reasoning agent. New validation logic can be added in Sherlock without touching ProcBot. The monolithic model is one large coupled system; the agentic system is composable.
How the Opstral agents collaborate
The collaboration runs as a continuous loop called OIAO: Observe, Investigate, Act, Optimize. Sentinel handles Observe and Investigate. ProcBot handles Act. Sherlock handles Optimize. The loop closes back to Sentinel through the learning record. Each phase has its own audit surface.
| OIAO phase | Agent | What it produces |
|---|---|---|
| Observe | Sentinel AI | Normalized signal stream from connected tools |
| Investigate | Sentinel AI | Root cause analysis with evidence, MOP selection, Action Ticket |
| Act | ProcBot | Executed change with full audit record (pre-checks, steps, validation, rollback if applicable) |
| Optimize | Sherlock | Outcome confirmation, recurrence flag, MOP effectiveness score, learning record |
For the architectural deep dive, see Inside the OIAO Architecture. For the MOP framework that makes agent execution safe, see Zero-Touch Runbook Execution. For the broader category split, see the AIOps platform comparison.
What an agentic AIOps system actually requires
Buying an agentic platform without the prerequisites in place is a fast path to disappointment. Five things have to be true on the customer side for the architecture to deliver.
- Real signal sources. The reasoning agent needs telemetry to reason over. If your environment is undermonitored, the agentic system inherits the gap. Detect-and-alert AIOps may be the foundational investment first.
- An MOP library. The execution agent runs procedures. If your operational knowledge lives in tribal memory and not in encoded MOPs, there is nothing for the platform to execute. Building the library is part of the implementation.
- Policy to bound action. The customer defines which MOPs the execution agent can run autonomously, under which conditions, in which environments, with which approval gates. Without policy, autonomy is recklessness.
- An audit appetite. The agentic system generates a lot of audit data. Someone has to look at it occasionally, not just store it. The audit trail is what makes the autonomy defensible.
- Federation with existing tools. The agents read from your monitoring, ITSM, identity, and business systems. The connector library needs to actually cover your stack. Check this before signing.
When agentic AIOps is the wrong investment
Honest disqualifiers, because the category will get oversold.
- Alert noise is the primary problem. Detect-and-alert AIOps fixes that faster.
- No MOP investment is possible. The execution agent has nothing to run.
- No policy bandwidth. Defining what the platform can do autonomously is real work. Skip the architecture if you cannot do it.
- Small operational scale. The audit and policy overhead may be heavier than the labor savings if you have a five-person ops team running ten services.
Agentic AIOps is for organizations whose operations bottleneck is human throughput in resolution, with enough scale to justify the policy and audit investment, and enough operational maturity to encode their knowledge as MOPs.
Frequently asked questions
Real architecture, recent marketing. The decomposition into reasoning, execution, and validation agents is what production-grade autonomous operations actually requires. The "agentic" framing has emerged in 2024-2025 partly to distinguish those products from ML-on-top-of-alerting plays. Both are happening simultaneously. Read the product, not the homepage.
In theory, yes. In practice, no. The audit, governance, and reliability properties of the decomposed system are hard to recover from a monolith. Regulators and ops leads ask "who decided" and "who acted" as separate questions. The architecture should answer them as separate questions.
Adjacent, not identical. MCP (Model Context Protocol) and similar agent-coordination protocols help expose tools to AI agents. Agentic AIOps is the application of multi-agent systems to operations work, using whatever protocols make sense for the integration surface. Some agentic AIOps platforms will adopt MCP for parts of their coordination. The category is defined by the operations use case, not the protocol.
Sentinel AI is the reasoning agent (observes, investigates, decides). ProcBot is the execution agent (runs MOPs under policy, holds integration credentials, records actions as Action Tickets). Sherlock is the validation agent (confirms outcomes, scores MOPs, detects recurrence). They collaborate through the OIAO loop, communicate via structured artifacts, and produce an immutable audit trail across the system. See Sentinel AI and AGENTS.md for the full agent capability declaration.