Results for “agent-management”

Deploy open models or custom weights from Model Garden to Agent Platform endpoints, check deployment status, verify serving endpoints, or clean up resources by undeploying models and deleting endpoints.

Add governance, safety, and trust controls to AI agent systems with policy enforcement, intent classification, and audit trails.

Create, update, refactor, explain, or review Microsoft Agent Framework solutions using shared guidance plus language-specific references for .NET and Python.

Manage custom Agent resources on the Gemini Enterprise Agent Platform via REST API, including creation, configuration, listing, updating, and deletion with support for files, skills, and tools.

Manage machine learning models in the Agent Platform Model Registry: list, describe, upload, update, and delete models and their versions.

Provides storage and environment conventions for NeMo-RL agents on Brev instances, ensuring large experiment outputs go to /ephemeral and secrets are loaded from .env.

Configure Azure API Management as an AI Gateway to govern AI models, MCP tools, and agents with policies for caching, rate limiting, content safety, and cost control.

Keep long-running agent work within 5-hour and weekly usage limits by checking usage between waves, pausing near the cap, and resuming only when the window is clear.

Reminds the agent to act as an interactive programmer using a live REPL for exploration and modification, with structural editing and todo list management.

Build AI applications on Microsoft Foundry using the Azure AI Projects Python SDK, including agent creation, tool integration, thread management, and evaluation.

Guides agents through the full lifecycle of NeMo-RL experiments: understanding recipes, launching reproducible runs, analyzing results, and preserving human oversight with git and TSV logs.