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Komodor | Pillar – Optimize Komodor | Pillar – Optimize

Optimize

Continuously Optimize Your Agents & Workflows

Every agent on the Komodor platform gets evaluated against real scenarios, with every change graded on the customer’s own traffic. Optimizations to model, prompt and tool usage ensure that your agents get faster, more accurate and cheaper to run over time.

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How do I get from functional agent to optimized fleet?

Validating a code change used to be a known path – write it, run it against tests and ship it once those passed. Agents work differently. Models, prompts, instructions, integrations and orchestration are all moving targets, shifting constantly. Any change to any of them, even ones not driven by your team, can degrade quality, cost, or speed. Komodor’s optimization engine has been built over three years to ensure agents are continuously evaluated and meet high standards of quality, performance and efficiency.

Grade Every Agent Against the Same Bar

All agents on the platform are continuously graded against reference standards. Every score — for an agent, a skill, or a single run — comes from the same unified grading engine, checked against baseline datasets.

  • LLM-as-a-Judge

    Define and edit eval suites and rubrics (criteria, weights, baselines); grade on accuracy, actionability, and token efficiency.

  • Human review queues

    Specific steps in a run transcript are automatically flagged for a person to check.

  • Unified grading engine

    One rubric-based scoring system covers agents, skills, and individual runs alike.

  • Baseline datasets

    Every score is checked against a reference standard for that use case, not graded in a vacuum.

Don’t Let AI Agents Break Your Budget

More runs, more tokens, more models in the mix — agent costs can spiral fast without real controls. Komodor enforces spend budgets, tracks token efficiency, and routes each step to the model that best fits it.

  • Spend budgets

    Set and enforce caps per agent or per workflow.

  • Token efficiency

    Measures and flags waste per tool, not just per agent.

  • Model routing

    Sends each step to the model that fits it, balancing cost against quality.

  • Cost trend visibility

    Tracks per-agent and per-run cost over time to highlight the results of optimization efforts.

Create Golden Scenarios for Optimal Coverage

Before it ships, every agent change is tested against golden scenarios — versioned unit tests for non-deterministic agents, each with a known-good outcome. Import ours or build your own — either way, no change ships without being regression tested.

  • Golden suites

    Out-of-the-box creation and management of test scenario sets, built over three years of testing against real incidents.

  • Regression gates

    A change only promotes if it clears the suite.

  • Development-led testing

    Scenarios get built and validated alongside the agent, not bolted on after.

  • Scenario types

    Predefined payloads or a full Kubernetes deployment, depending on what the agent needs to prove.

Automatically Run Shadow Experiments, Safely

Just like you don’t commit a PR straight to main, you don’t run agent experiments in production. Komodor runs them in shadow instead — a challenger variant compared against the champion on real traffic, with zero blast radius.

  • Experiment catalog

    Create and track shadow variants side-by-side with production.

  • Promotion/retirement

    Promote a winning variant or retire a failing one, evidence attached.

  • Automation policies

    Set rules for when experiments auto-promote, pause, or require a human review.

  • Proposed changes

    Get concrete diffs tied to specific opportunities, ready to review.

Navigate the Messy
Seas of Production

With every run, Komodor expands the live knowledge graph of your environment: services, dependencies and previous remediation attempts. That context drives improvement over future runs.

  • Resource dependency mapping

    A live picture of how your services and infrastructure connect, kept current with every run.

  • Causality chains

    Connect an incident to what it actually affects, so agents reason about root cause, not just symptoms.

  • Verified and failed remediations

    Every fix that worked, and the ones that didn’t, becomes part of the graph, so agents stop retrying what’s already been ruled out.

The Gap Between an Agent and a Reliable Operational Process

Solution Modules

An incident response workflow orchestrates agents across multiple steps from trigger to outcome.

With Komodor, agents get the step-by-step orchestration needed to solve real operational pain for Platform, SRE and DevOps teams. Three solutions — AI SRE, Cost Optimization, and AI Software Operations — each package their own solution modules, containing the agents, steps, tools and integrations required, already validated and ready to run.

Ingest & Produce
Synthesize & Analyze
Signal Agent
Investigate
Agent A VS Agent B
Remediate
Proposer Executor
Verify
No agent needed
Notify
  • Out-of-the-box agent
  • Bring-your-own agent
  • VS Competing agents
  • Tandem agents

Optimized Agents, Under Shared Infrastructure

Every agent in your fleet – whether built with Komodor, imported, or pulled from the catalog – is automatically scored, permissioned, and secured by the same systems.

Komodor | Pillar – Optimize

Build

However it got here. Built from scratch, imported, or pulled from the catalog, every agent lands in the same fleet the moment it’s live

Komodor | Pillar – Optimize

Run

Wherever it runs – your cloud, on prem or Komodor cloud – manage every agent centrally as a unified, enterprise-wide fleet.

Komodor | Pillar – Optimize

Optimize

Every run by every agent — yours and ours — is evaluated by LLM-as-judge for continued performance and cost improvements over time.

Komodor | Pillar – Optimize

Govern

Every action by every agent running is automatically permissioned under the same RBAC and approval rules your team set.

Ready to run agentic
operations that scale with you?

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Komodor | Pillar – Optimize