SRE teams are moving from AI-assisted troubleshooting to agentic operations: environments in which AI agents can monitor signals, investigate incidents, coordinate workflows, recommend remediation, and execute approved operational tasks.
The shift extends beyond incident response. Production teams also manage deployment risk, alert quality, reliability improvements, cloud costs, infrastructure drift, CI/CD failures, and repetitive operational work that consumes engineering time without necessarily requiring human judgment at every step.
What Is an Agentic Operations Platform?
An agentic operations platform uses autonomous or semi-autonomous AI agents to perform production engineering and IT operations tasks.
Traditional observability identifies what is happening. AIOps adds capabilities such as anomaly detection, event correlation, and alert reduction. Agentic operations introduce another layer: systems that can investigate, decide what should happen next, invoke operational tools, and continue working toward an outcome.
Autonomy also creates new operational requirements. Agents need reliable production context, limited permissions, safe credential access, human approval for consequential actions, audit trails, and mechanisms for evaluating whether their decisions are accurate. This means the agent itself is only one part of the system. The surrounding context, governance, execution, and verification layers are equally important.
10 Best Agentic Operations Platforms for SRE Teams in 2026
1. Komodor – Agentic Operations for Production Environments
Komodor is an Agentic Operations Platform for Production that helps SRE, DevOps, and Platform Engineering teams build, run, govern, and optimize AI agents across production environments.
The platform combines ready-to-run operational agents with infrastructure for custom and externally built agents, allowing organizations to manage different types of production automation through a shared operational layer. Current use cases span AI SRE, cost optimization, and AI software operations, including incident investigation, alert intelligence, proactive reliability, change risk, production readiness, cloud and Kubernetes optimization, and CI/CD remediation.
The platform also provides infrastructure for running agent fleets. Agents can be triggered by alerts, schedules, pull requests, chat interactions, or other agents and can participate in multi-step operational workflows.
Governance applies across the agent environment. Teams can define scoped permissions, broker credentials, require approvals, set budgets and rate limits, and maintain audit records of agent activity.
Komodor also includes mechanisms for evaluating and optimizing agents over time, including golden scenarios, regression testing, shadow experiments, model routing, and token-efficiency analysis.
Key capabilities include:
- Prebuilt production operations agents
- Custom and imported agents
- AI SRE workflows
- Incident investigation and remediation
- Alert intelligence
- Cost optimization
- Production readiness analysis
- CI/CD remediation
- Shared agent memory
- Production knowledge graph
- Multi-agent orchestration
- Scoped RBAC
2. Resolve AI – AI SRE Agents for On-Call Operations
Resolve AI focuses on putting autonomous SRE agents directly into day-to-day production operations. Its agents can participate in on-call workflows, investigate alerts, analyze production systems, recommend mitigation, and perform recurring operational tasks.
The platform separates several types of agent activity. On-call agents evaluate incoming alerts and determine which events require deeper investigation. Incident agents can analyze active production problems while engineers are responding. Background agents can perform recurring operational work outside active incidents.
Relevant capabilities include:
- AI on-call agents
- Alert triage
- Autonomous incident investigation
- Parallel hypothesis testing
- Production mitigation
- Recurring background agents
- Deployment monitoring
- Shared operational context
- Coding-agent handoffs
- Root cause investigation
3. Datadog Bits AI – Agentic Investigation Across Observability Data
Datadog extends its observability platform with Bits AI agents that can investigate production problems using the large amount of telemetry already collected inside the environment.
Bits AI can reason across metrics, logs, traces, infrastructure state, network information, deployment metadata, monitors, and other operational signals. This breadth is valuable because production incidents frequently span several technical layers.
Relevant capabilities include:
- Autonomous incident investigation
- Metrics, logs, and trace analysis
- Infrastructure context
- Network information
- Change correlation
- Root cause analysis
- Conversational operations
- Remediation assistance
- Custom agent workflows
- Agent evaluation
4. Dynatrace – Agentic Operations With Causal Production Context
Dynatrace combines its established observability and causal-analysis capabilities with a growing collection of operational AI agents. Its approach brings together deterministic analysis and generative reasoning.
That distinction matters in production operations because some questions are well suited to AI interpretation, while others should remain grounded in known topology, dependencies, telemetry, and causal relationships. Dynatrace provides specialized agents for SRE, development, and security workflows. These agents can help investigate incidents, identify root causes, assess operational context, and participate in remediation processes.
Relevant capabilities include:
- Specialized SRE agents
- Automated incident triage
- Root cause analysis
- Causal production context
- Autonomous remediation workflows
- Multi-cloud agent orchestration
- No-code agent configuration
- Observability-driven decisions
- Human oversight
- Operational automation
5. incident.io – AI Investigation Within Incident Response Workflows
incident.io approaches AI SRE from the incident-management process. Its Investigations capability begins analyzing production issues when incidents are declared, using telemetry, deployment information, code changes, documentation, and previous incident history to develop root cause hypotheses.
This creates a collaborative workflow between AI and human responders. The agent can investigate while engineers coordinate the operational response through collaboration tools. Rather than functioning as a separate troubleshooting application, AI analysis becomes part of the same incident process used for roles, communication, timelines, escalation, and resolution.
Relevant capabilities include:
- Autonomous incident investigation
- Root cause hypothesis generation
- Change correlation
- Historical incident context
- Collaborative response workflows
- Incident timelines
- AI-generated summaries
- Communication management
- Post-incident workflows
- On-call integration
6. Rootly – AI-Assisted Incident Investigation and Response
Rootly combines AI SRE capabilities with incident management, on-call operations, service ownership, communication, and post-incident workflows. Its AI SRE analyzes signals such as telemetry, recent deployments, code changes, configuration updates, and historical incidents to produce possible root causes and recommended next steps.
The system also provides confidence information and supporting evidence, which helps responders understand why a particular hypothesis has been proposed. That explainability is important in production environments where a remediation recommendation may lead to a consequential action.
Relevant capabilities include:
- AI SRE investigations
- Root cause hypotheses
- Confidence scoring
- Deployment correlation
- Service ownership context
- Similar incident analysis
- Remediation recommendations
- Human approval
- Incident communication
- Retrospective automation
7. PagerDuty – Agentic Operations Around On-Call and Escalation
PagerDuty is extending its incident management and on-call platform through SRE agents and specialized operational AI.
Its SRE Agent can investigate incidents, retrieve information from connected monitoring and observability systems, analyze available evidence, and suggest troubleshooting or remediation steps. A key part of the approach is integration with existing escalation policies.
Relevant capabilities include:
- SRE Agent
- Alert triage
- Incident investigation
- Escalation-policy integration
- Suggested remediation
- Observability integrations
- MCP connectivity
- Incident documentation
- On-call scheduling
- Post-incident support
8. New Relic – Agent-Ready Observability and Autonomous SRE
New Relic has expanded its observability platform around two related requirements: providing autonomous operational capabilities and making production telemetry accessible to agents built elsewhere.
Its autonomous SRE capabilities can investigate incidents and reduce manual operational work. At the same time, agent-ready observability allows external or internally developed agents to retrieve production context from New Relic rather than requiring the observability platform itself to own every autonomous workflow.
Relevant capabilities include:
- Autonomous SRE workflows
- Agent-ready observability
- Production telemetry
- Incident investigation
- Runtime context
- APIs for external agents
- Custom agent integration
- Operational automation
9. ServiceNow ITOM – Agentic Workflows Across Enterprise IT Operations
ServiceNow brings agentic operations into its wider IT Operations Management environment. AI agents can participate in alert triage, impact analysis, root cause investigation, service mapping, remediation, configuration management, and other IT operations workflows.
This broader scope differentiates ServiceNow from platforms focused predominantly on SRE incident response. Its agentic workflows can coordinate tasks across ITOM, ITSM, CMDB data, service ownership, infrastructure information, and enterprise workflow automation.
Relevant capabilities include:
- Autonomous alert triage
- AIOps agents
- Root cause investigation
- Service Mapping agents
- Business-impact analysis
- Remediation workflows
- Change-impact analysis
- ITOM and ITSM integration
- CMDB context
- Cross-platform automation
10. BigPanda – Agentic Automation for High-Volume IT Operations
BigPanda applies agentic automation to high-volume operational environments where large numbers of alerts and incidents need to be processed before they reach senior technical teams.
Its L1 Agent can analyze incoming operational information, route incidents, summarize context, prepare escalation packages, and recommend next steps. The platform also provides AI-assisted incident investigation for more complex cases.
Relevant capabilities include:
- Agentic ITOps
- Automated L1 operations
- Alert correlation
- Incident routing
- Escalation preparation
- AI-assisted investigation
- Root cause context
- IT Knowledge Graph
- No-code workflows
- ITSM integration
What SRE Teams Can Automate With AI Agents
Agentic operations does not require giving an AI system unrestricted production control.
A more practical approach is to automate progressively, starting with work where the outcome can be checked easily and increasing autonomy as confidence improves.
Alert Triage and Enrichment
Incoming alerts frequently lack enough context for efficient human response.
An agent can collect:
- Related alerts
- Recent deployments
- Service ownership
- Change history
- Relevant metrics and traces
- Similar incidents
- Applicable runbooks
This allows low-value signals to be classified quickly while meaningful alerts reach engineers with a much richer evidence package.
Incident Investigation
Incident investigation is one of the clearest uses for operational agents.
An agent can search logs, traces, metrics, infrastructure events, deployment records, configuration changes, previous incidents, and source repositories simultaneously.
Multiple agents can also investigate competing hypotheses in parallel.
This can reduce the time between detecting a problem and identifying the likely technical cause.
Known Remediation
Some operational fixes are predictable enough for controlled automation.
Examples include:
- Restarting a failed workload
- Rolling back a deployment
- Scaling a resource
- Running an approved script
- Clearing a known queue condition
- Reapplying configuration
The important requirement is verification.
After executing the action, the system should confirm that the target condition improved instead of assuming that successful command execution equals successful remediation.
Deployment and Change Monitoring
Agents can follow production changes after release and compare operational behavior against expected conditions.
This can help SRE teams identify regressions earlier and connect unexpected behavior with the change that introduced it.
AI can also support production-readiness checks before deployment by looking for missing operational dependencies, weak alert coverage, or other known reliability gaps.
Reliability and Cost Optimization
Not every useful operational agent needs to respond to an incident.
Agents can continuously search for:
- Repeated reliability issues
- Excessive resource allocation
- Idle infrastructure
- Poor alert configuration
- Capacity problems
- Recurring deployment failures
- Preventable operational toil
This turns agentic operations from reactive troubleshooting into continuous production improvement.
How to Evaluate Agentic Operations Platforms
A useful evaluation should test more than whether an AI agent can explain one carefully selected incident.
Production operations require repeatability, governance, and evidence.
Production Context
Determine what the agent can actually understand about the environment.
Useful context can include:
- Services and dependencies
- Infrastructure
- Ownership
- Telemetry
- Deployments
- Source changes
- Previous incidents
- Runbooks
- Tickets
- Cloud resources
The broader the autonomy, the more important accurate context becomes.
Autonomous Execution
Ask what the system can do after reaching a conclusion.
Some platforms primarily investigate.
Others can execute runbooks, call operational APIs, change infrastructure, modify code, coordinate additional agents, or verify remediation.
The right level depends on how much operational autonomy the organization intends to introduce.
Multi-Agent Coordination
Different operational tasks may require different agents.
An investigation agent can identify the likely cause.
A remediation agent can prepare a response.
A deployment agent can execute the approved change.
A verification agent can determine whether production recovered.
Separating these roles also reduces the risk of allowing one agent to diagnose, execute, and declare its own work successful without independent checks.
Permissions and Production Safety
Production agents need stricter controls than ordinary AI assistants.
Teams should examine:
- Scoped identities
- RBAC
- Secret handling
- Tool permissions
- Read and write separation
- Environment boundaries
- Human approvals
- Rate limits
- Budget controls
- Audit trails
An investigation agent may need extensive read access without any ability to modify production.
Evaluation and Verification
Operational agents need measurable performance.
Relevant questions include:
- How often is the root cause correct?
- How frequently does the agent escalate unnecessarily?
- How often is human intervention required?
- Does remediation actually resolve the incident?
- Are newer agent versions better than previous ones?
- What does each completed autonomous task cost?
Agent activity alone is not a meaningful success metric.
The useful measure is whether the system improves operational outcomes.
Frequently Asked Questions
What is the difference between AIOps and agentic operations?
AIOps typically analyzes operational data to detect anomalies, correlate events, prioritize alerts, and surface insights. Agentic operations goes further by allowing AI agents to investigate problems, choose tools, coordinate workflows, recommend responses, and potentially execute approved operational actions.
Can AI agents safely make production changes?
They can perform selected production actions when appropriate controls are in place. Organizations should use scoped permissions, approved tools, audit trails, environment restrictions, automated validation, and human approval for higher-impact actions. Autonomy should increase only where outcomes can be reliably verified.
Do agentic operations platforms replace observability tools?
Usually not. Operational agents depend on telemetry and production context from observability systems. Some agentic platforms include these capabilities, while others integrate with existing monitoring, tracing, logging, incident management, and cloud systems.
What should SRE teams automate first?
Strong early use cases include alert enrichment, incident evidence collection, deployment monitoring, routine diagnostics, reporting, known runbook execution, and other repetitive tasks with clear success criteria. These workflows allow teams to establish confidence before introducing more consequential autonomous remediation.
What features matter most when comparing agentic operations platforms?
Important areas include production context, autonomous execution, integration coverage, multi-agent orchestration, agent permissions, approval workflows, auditability, outcome verification, and evaluation of agent performance. The correct balance depends on how much operational authority the organization intends to delegate.
Which agentic operations platform supports both prebuilt and custom production agents?
Several platforms are expanding beyond fixed AI assistants toward customizable agent workflows. Komodor is one example, combining ready-to-run production agents with infrastructure for building or importing custom agents and managing them through shared context, orchestration, governance, evaluation, and operational controls.
