agent-framework/dotnet/samples/02-agents/Harness/Harness_Step05_Loop

What this sample demonstrates

This sample demonstrates how to wrap a HarnessAgent with the LoopAgent decorator to re-invoke the agent until a configured LoopEvaluator decides to stop. A single decorator covers the common looping patterns — you just plug in a different evaluator (and optionally switch on fresh-context mode).

The HarnessAgent pre-configures function invocation, per-service-call chat history persistence, and in-loop compaction, so each demo only supplies the chat client, token limits, and instructions, then wraps the result with a LoopAgent.

Looping patterns showcased

The program runs four demos sequentially, each driven by a different evaluator:

#	Pattern	Evaluator	Notes
1	Completion-marker ("Ralph"-style) loop	CompletionMarkerLoopEvaluator	Re-invokes until the agent emits <promise>COMPLETE</promise>. Uses FreshContextPerIteration = true to restart each pass from the original task plus the aggregated feedback log on a new session, and includes the {last_response} placeholder in the feedback template so the agent sees its previous suggestion even though each pass starts fresh.
2	Delegate predicate (todos remaining)	DelegateLoopEvaluator	Loops while the built-in TodoProvider still has open items. The provider is fetched from the agent via GetService<TodoProvider>() and queried against the loop's current session.
3	AI judge	AIJudgeLoopEvaluator	A second IChatClient judges whether the original request was fully answered and continues while the answer is "no", injecting its gap analysis as the next input.
4	Approval heuristics + loop	DelegateLoopEvaluator + ToolApprovalAgent	Combines the ToolApprovalAgent auto-approval heuristics (AutoApprovalRules) with the loop, so a looped agent auto-approves tool calls instead of stalling on a pending approval.

MaxIterations caps every loop so it always terminates even if the evaluator never stops.

Evaluator mapping (Python → .NET)

The Python sample in microsoft/agent-framework#6174 exposes several distinct loop classes. In .NET these collapse into one LoopAgent that consumes evaluators:

Python	.NET
Ralph loop (completion marker)	LoopAgent + CompletionMarkerLoopEvaluator
Ralph loop (fresh context each pass)	LoopAgent + CompletionMarkerLoopEvaluator + FreshContextPerIteration = true
Callable / predicate loop	LoopAgent + DelegateLoopEvaluator
AI judge loop	LoopAgent + AIJudgeLoopEvaluator

Prerequisites

Before running this sample, ensure you have:

1. An Azure AI Foundry project with a deployed model (e.g., gpt-5.4)
2. Azure CLI installed and authenticated (az login)

Environment Variables

Set the following environment variables:

# Required: Your Azure AI Foundry project endpoint
export AZURE_AI_PROJECT_ENDPOINT="https://your-project.services.ai.azure.com/api/projects/your-project"

# Optional: Model deployment name (defaults to gpt-5.4)
export AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-5.4"

Running the Sample

cd dotnet
dotnet run --project samples/02-agents/Harness/Harness_Step05_Loop

What to Expect

The program runs the four demos in order. Each loop is executed with RunStreamingAsync, so output is printed live and every re-invocation of the inner agent is marked with a --- run N --- header (detected via a change in the streamed ResponseId) — this lets you see exactly when the LoopAgent loops. Each streamed message is prefixed with User: or Agent: based on its role, so the loop's on-behalf-of feedback messages (surfaced as User turns) are visually distinct from the agent's responses (Agent). Each demo finishes by printing its aggregated final response. Demo 4 also prints an Auto-approving: ... line each time the ToolApprovalAgent heuristic approves the DeployService tool call, showing how approval-aware agents integrate with the loop.