KAgent: குபெர்னெட்டஸின் மீது கிளவுட்-நேட்டிவ் ஏஜென்ட் சார்ந்த செயற்கை நுண்ணறிவை உருவாக்குவதற்கான ஒரு கட்டமைப்பு

Explains the core architecture of kagent and how it handles agentic tasks.

- Details the interaction between instructions, tools, and skills.

- Contrasts metadata-based A2A skills with executable container-based skills.

- Describes how agents use context compaction to manage long-running memory usage.

- Explains how to manage long conversations using event compaction and summarization techniques.

- Details best practices for creating and containerizing custom skills for agent reuse.

- Outlines a practical workflow for testing agent skills in a local Kubernetes cluster.

- Outlines best practices for writing effective system prompts using templates and ConfigMaps.

- Explains how MCP helps connect agents to external data and tools.

- Discusses strategies for chaining agents together using the MCP and A2A protocols.

- Outlines using kmcp to develop and deploy MCP tools within Kubernetes environments.

- Compares built-in tools with custom MCP server integrations using the kmcp platform.

- Explains how to configure Human in the Loop approval gates for destructive agent actions.

- Compares Python and Go runtimes based on startup time and resource consumption.

- Compares the performance benefits and use cases for Python versus Go agent runtimes.

- Explains how to configure event retention and token thresholds for efficient memory usage.

- Summarizes how agents use vector similarity search to save and retrieve conversation context.

- Describes how kagent uses vector search for long-term memory across sessions.

- Explains how to use sandbox agents to isolate processes and restrict network access.

- Details why kagent is considered declarative and Kubernetes native compared to other frameworks.

- Describes how to register agents as tools for modular cross-agent communication.

- Details how to use cross-namespace tool references to scale your agent ecosystem.

- Explores how chaining multiple agents together enables complex automated problem solving.

- Describes how to use the Ask User tool for clarifying complex agent tasks.

- Focuses on how Kubernetes native features influence agent design and operational scaling.

- Discusses strategies for managing production secrets and API keys safely in agents.

- Explains how to monitor agent health and performance in a Kubernetes cluster.

- Describes a production rollout strategy using kagent features.

- Analyzes security trade-offs when using shared namespaces for agent tools.