337 posts · Page 15 of 29. ← Blog
Turn a workflow run into a tree of OpenTelemetry spans — workflow.build, workflow.run, executor.process, edge_group.process, message.send — with one setup call.
Trace a workflow with configure_otel_providers and a root span. The framework emits workflow.build, workflow.run, executor.process, edge_group.process, message.send spans.
Reach a Foundry-hosted model through the OpenAI-compatible Responses API by configuring a plain openai.Client with three request options.
Reach a Foundry model through the OpenAI-compatible API by configuring an openai.Client with base URL, an Azure token credential, and the ai.azure.com scope.
Enough to reconstruct, never enough to leak. The audit event schema problem under §164.312(b), and how to solve it without conflating the audit sink with the PHI sink.
Declare the orchestration in YAML instead of Python — ordered actions load at runtime via WorkflowFactory, with agents resolved by name and PowerFx expressions for logic.
Declare a workflow in YAML and load it with WorkflowFactory. Ordered actions call agents resolved by name, with PowerFx expressions for prompts and variables.
Point the same agent at an Azure AI Foundry project endpoint using the project Responses API mode.
Run an agent against an Azure AI Foundry project: foundryprovider.NewAgent plus ModelDeployment selects project Responses API mode from the project endpoint.
Every LLM-backed agent in this platform has a deterministic rule-based fallback. The case always finalises. The fallback isn't a workaround — it's the contract.
Five interfaces hold the whole platform together. The 30-line orchestrator closure that makes the rest of the architecture testable, auditable, and safe to evolve.
Shared workflow state: any executor writes a keyed value, any later executor reads it back — so big payloads never have to be threaded through every message on the edge.
Share workflow state with ctx.set_state and ctx.get_state so big payloads skip the edge. Writers see updates immediately; other nodes see them the next superstep.
The same agent primitive as the Foundry lessons, backed by the Anthropic (Claude) provider instead of Azure AI Foundry.
A provider swap onto Anthropic Claude: build an anthropic.Client, pass it to anthropicprovider.NewAgent, and run the identical agent surface.
PostgreSQL row-level security as HIPAA defence in depth. Why fail-open application filtering isn't enough, and how 'append-only at DB GRANTs' carries more of the §164.312(b) burden than people realise.
An agent can be a graph node directly — no custom Executor subclass. Writer drafts, a direct edge hands its output to Reviewer, and streamed tokens group by author.
Use an agent as a workflow executor node directly, no subclass needed. Wire Writer to Reviewer with an edge and group streamed AgentResponseUpdate tokens by author.
This lesson swaps the LLM behind an agent for another agent, reached over the A2A protocol instead of a model API.
Back a Go agent with another agent over the A2A protocol: resolve the remote card, open a gRPC client, and wrap it with a2aprovider.NewAgent.