azure-agents-learning-sdk

Native reinforcement learning SDK for AI agents. An in-process learner optimizes a small, interpretable policy over discrete agent configuration choices (prompt variants, retrieval-k, tool selection strategies, …).

How it works

The SDK improves agents without LLM weight fine-tuning. There are no GPU fine-tune jobs and no opaque update cycles — just three pieces that run in your existing Python process:

1. The policy is a softmax distribution over N discrete actions (e.g., "use prompt template A", "use template B"). It lives in Python and updates in milliseconds.

2. Each episode is judged by three Azure AI Evaluation evaluators — IntentResolutionEvaluator, TaskAdherenceEvaluator, and TaskCompletionEvaluator — whose scores are combined into a single scalar reward.

3. A REINFORCE-with-baseline learner updates the policy logits directly from logged episodes. Updates are tiny gradient steps that run on CPU and persist through a pluggable store — in-memory or local files by default, with Cosmos DB optional.

Every episode, reward, run, and deployment is captured by the configured store — in-memory or local files by default, or Cosmos DB — giving you a complete lineage and audit trail of how the policy evolved over time.