agent-management

Declarative AI agent management uses YAML files to define agent configurations, enabling consistent and repeatable deployments. You specify prompts, tools, and memory configurations in YAML, then apply the file to create or update agents across a fleet.

Yes, the Letta AI agent framework integrates directly to support fleet management. This integration provides detailed introspection of agent states and associated tools, allowing you to scale and coordinate multiple specialized AI agents effectively.

Creating an AI agent involves defining its name, description, and system prompt within a YAML configuration. Applying this declarative configuration initiates the agent creation process, setting up its initial state and associated tools for immediate use.

Declarative agent configuration supports full lifecycle management, including updating and deleting agents. You can also export and import agent configurations, ensuring you can modify or remove agents from your fleet as operational needs change.

Declarative YAML management for AI agents currently focuses on individual agent configurations and lifecycle operations within the Letta framework. It does not inherently handle complex inter-agent communication routing or dynamic resource scaling outside of fleet configuration boundaries.