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Autonomous, slice-aware AI agent that brings real-time intelligence and decision-making into Kubernetes operations.

KubeAgent adds intelligence and autonomy to Kubernetes by detecting issues, enforcing policies, and executing self-healing actions — all in real time.

AI Agent Layer
(Avesha KubeAgent)

Adds an intelligence layer on top of Kubernetes to power autonomous operations.

Detects and resolves issues autonomously (e.g., self-healing, noisy neighbor mitigation)

Enforces smart routing for compliance and performance

Surfaces observability-driven enforcement (cost, resource abuse, etc.)

Recommends actions based on policies, usage patterns, and behavior

Powers model-aware infrastructure for future GPU workloads (EGS readiness)

Automation Layer
(Current/Traditional Stack)

Executes zero-touch automation across your Kubernetes environments.

Zero-touch namespace and slice creation

Policy-as-code + RBAC integration with existing identity providers

Automated provisioning of services (DNS, Git, Redis, etc.)

Time-bound access controls and audit hooks for internal compliance

Seamless multi-cluster scaling

KubeAgent + Automation = Our Differentiator

Together, they enable truly autonomous cloud-native operation

KubeAgent brings actionability and intelligence.

Automation delivers speed and consistency.

KubeAgent
Architecture

Architecture Overview

KubeAgent sits on top of the Avesha platform, delivering autonomous monitoring, intelligent decision-making, and self-healing across



KubeAgent
Workflow

KubeAgent Workflow

Data Collection

Continuously collects metrics, logs, and events across Kubernetes clusters. Focus areas include:

  • CPU, memory, performance
  • Access patterns and latency
  • Slice health and network traffic
  • Resource utilization and cost

Pattern Recognition

AI engine analyzes data to:

  • Establish performance baselines
  • Detect policy violations or threats
  • Forecast usage patterns
  • Identify optimization zones
  • Correlate patterns across environments

Decision-Making & Enforcement

Based on real-time insights:

  • Enforces custom SLAs and policies
  • Calculates risk scores
  • Automates slice optimization and node actions
  • Learns from prior decisions to improve outcomes

Autonomous Actions

Executes real-time decisions such as:

  • Healing degraded slices
  • Rebalancing workloads
  • Improving performance
  • Scaling resources
  • Reducing costs through smart placement