Go From Isolated Agents to Autonomous Operations, Fast
With Komodor, SRE, DevOps and Platform teams get autonomous agentic workflows for all their most important work – ready to deploy out of the box. Our Solutions package repeatable, efficient and transparent end-to-end processes with measurable outcomes that you can trust in production.
Anatomy of a Solution Module
Inside every Solution Module are prepackaged workflows for the most common operational processes. Every workflow is run by agents – ours out of the box, or yours – each carrying one or more capabilities that do the actual work, with deterministic rules optionally layered onto any step to route, branch, gate, or require approval.
Each Solution Module gets its own relevant views, not a generic dashboard. For example, Incident Management & Troubleshooting gets an incident feed including the signals, root cause analysis, remediation actions and postmortems. Kubernetes Cost Optimization gets allocation, right-sizing, and pod placement.
Input/Output Contract
Every step declares what it needs and what it produces so agents hand off cleanly, and results are predictable.
Agents: Out-of-the-Box & Bring-Your-Own
Any step can be filled by a validated agent we ship, or one you build yourself.
Steps
The ordered stages inside every workflow: ingest, analyze, act.
Routing
Decides which agent handles a given input by fixed rule, by classifier, or by another agent’s judgment.
Shared Context
Every agent in a module works from the same data so they’re coordinating, not duplicating each other’s work.
Continuous Optimization
Every workflow and step is automatically evaluated and scored, surfacing optimization opportunities, and giving you full visibility into every run.
Operational Workflows In Action
Six steps, one end-to-end workflow — from trigger to outcome.
- 01
Ingest & Produce
Datadog (mon-checkout-p95-latency), Grafana Loki (241× retry_exhausted), and a Kubernetes rollout of payments-service v2026.05.21.4 fire independently, 7–14 minutes apart.
- 02
Synthesize & Analyze
Linked on shared fields (service:checkout-api, service:payments-service, deploy:payments-service), high confidence, one incident, not three.
- 03
Investigate
Datadog Alert Triage completes in 43 seconds; Kubernetes Change Correlation keeps scoring live. Root cause: the payments rollout caused a retry storm on checkout-api.
- 04
Remediate
Roll back the payments service to the previous revision; longer-term, review auto-generated PR #1887 for the underlying database-access issue.
- 05
Verify
Latency back to normal, no error logs, Kubernetes deployment healthy.
- 06
Notify
Incident closed in PagerDuty, resolution posted to Slack.
- 01
Ingest
AWS Cost Explorer API pulls S3 spend by bucket, region, and account, daily, tagged by team/project.
- 02
Synthesize
S3 totals $47K/month — storage ($31K), requests ($9K), transfer ($7K). Top 3 buckets own 72% of spend.
- 03
Correlate
CloudTrail access logs × storage class × object age surface two findings: 400GB sitting in Standard with zero reads in 90 days; 1.2TB accessed so rarely it belongs in Infrequent Access.
- 04
Raise Opportunities
Two findings flagged, high confidence, based on 90-day access history: cold data to Glacier Deep Archive, infrequent data to S3-IA.
- 05
Remediate
Lifecycle rules applied for the cold data; infrequent buckets transitioned via Terraform PR #412.
- 06
Validate
Seven days monitored: no errors, no retrieval complaints, savings tracked against a $24.20/month projection on this bucket group.
- 07
Notify
Ticket closed, summary posted to #cloud-costs, baseline updated for next cycle.
- 01
Trigger
Argo’s canary step fires and posts a deploy event to the module’s endpoint.
- 02
Assess Blast Radius
The workflow determines the canary only affects the checkout service and its immediate dependencies, so it knows exactly which signals matter.
- 03
Soak & Compare
It watches canary vs. baseline latency, errors, and cost together. The canary is 10ms faster at p50, but its error rate ticks up, and infra cost per request rises 20%.
- 04
Verdict
Because Komodor weighs all three signals jointly (not latency alone), it flags this as “roll back” rather than letting the speed-win mask the cost/error regression.
- 05
Human Approval
Since rollback is a write to production, it’s proposed, not auto-executed. An engineer reviews the comparison and approves it, and a narrowly scoped rollback credential executes it.
- 06
Record
The module logs the change, its blast radius, the compared signals/window, and the verdict, with the underlying runs open for inspection.
- 07
Notify
The release team is notified about the progressive rollout failure and subsequent rollback
Frequently asked questions
A Solution Module is a ready-to-run agentic workflow for one operational job, such as incident management, cloud cost or CI/CD health. Each module packages the workflows, specialist agents, integrations and views that the job needs, so it can run in production from day one. Modules are grouped into three Solutions: AI SRE, Cost Optimization and AI Software Operations.
Docs link: Modules & workflows
Komodor has three Solutions, each with four modules:
- AI SRE: Alert Intelligence, Incident Management, Troubleshooting and Proactive Reliability Optimization.
- Cost Optimization: Kubernetes Cost, Cloud Cost, Observability Cost and Tokenomics Optimization.
- AI Software Operations: CI/CD Health, Production Readiness, Standardization and Change Intelligence & Risk Control.
Docs link: Modules & workflows
No. It’s one platform with two ways in. Solution Modules run on the same platform you’d use to build your own agents, so your agents can run as steps inside Komodor’s workflows. They get the same evaluation, guardrails and cost tracking. Some teams start with a module and extend it, and others start by bringing their own agents.
Docs link: Concepts & glossary
Yes. No module is a black box. Each workflow is a defined sequence of steps, and each step declares what it takes in and what it produces. Every run records its trigger, tool calls, reasoning and outcome, so you can see which agent handled each step and why it reached its conclusion.
Docs link: Runs & evidence
Yes. Choose which agent handles each step of a workflow: one of Komodor’s, or one you built or imported. You can also add your own skills and knowledge to the agents Komodor ships. Rules can route work, gate a step or require approval, and you can switch models and set spend limits per agent.
Docs link: Modules & workflows
Not unless you allow it. Every module runs under the same governance as the rest of the platform. By default, read-only work runs on its own, and changes wait for a person to approve them, with the agent’s reasoning attached. You can keep a module read-only, or limit it to opening pull requests, while you build trust. Later you can let low-risk actions run automatically.
Docs link: Approvals
Yes. Tokenomics Optimization, part of the Cost Optimization Solution, analyzes model costs, token usage, caching and other AI cost levers. It’s separate from the platform’s Optimize capabilities, which improve the agents running on Komodor, including what they cost to run.
Yes. Every module runs on the platform’s shared memory and context layer: a live knowledge graph of your services and dependencies, your knowledge base, and memory from past runs. Memory can be shared across agents, so lessons from one workflow are available to others. Each completed run adds to what later runs know, so no workflow starts from zero.
Docs link: Memory
Governed By Default
Every module runs under the same governance layer — whether the agents doing the work are ours, or ones you built from scratch or imported.
Human approval
Risky actions always pause for approval, with full context on what the agent wants to do and why.
Scoped access
Every agent’s access is scoped — what it can read, what it can write, and where.
Spend caps
Spend is capped per agent or per team.
Full audit trail
Every run is logged, and standards apply account-wide.
Ready to move from isolated agents to autonomous workflows you can trust in production?
