| name | agent-ops |
| description | operationalization strategies for agents (AgentOps). Use this to manage internal/external tools, optimize agent "brain" prompts, and handle task decomposition. |
Agent Ops (AgentOps)
Goal
Implement a robust framework for the efficient operationalization of agents, moving beyond simple prototypes to reliable production systems .
Core Components
Successful AgentOps requires managing the following "agentic" dependencies:
- Tool Management: Coordinating internal and external tools and APIs.
- Agent Brain Prompt: Defining the core goal, profile, and instructions that drive the model.
- Orchestration: Managing the loops of reasoning and planning.
- Memory: Handling both short-term working memory (sessions) and long-term storage.
- Task Decomposition: Breaking complex user objectives into manageable sub-tasks.
The Ops Relationship
AgentOps is a subcategory of GenAIOps and inherits best practices from its predecessors:
- DevOps: Foundation for deterministic software, CI/CD, and version control.
- MLOps: Management of non-deterministic models and data pipelines.
- PromptOps: Versioning and optimization of the instruction sets.
Production Best Practices
- Observability (Traces): Use "traces" to log the inner workings of an agent, including internal steps and tool calls, to facilitate debugging.
- Feedback Loops: Instrument your agent to capture human feedback (e.g., thumbs up/down) to calibrate performance and autoraters.
- Hybrid Instrumentation: Track both business-level KPIs (goal completion rate) and application telemetry (latency, errors).
