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Use the smallest workflow the query requires, and add agentic behavior only where it earns its cost. A fixed retrieve-then-generate pipeline handles predictable questions that map to one search against one index. Agentic retrieval, where the model decides whether to search again, fits questions that must be decomposed, routed across sources, or refined through tool results. On Azure, the Durable Extension for Microsoft Agent Framework provides persisted agent sessions and recoverable multi-agent orchestration on Azure Functions. Redis is worth adding for low-latency conversation context, searchable retrieval memory, semantic caching, or a reliable stream broker. It should not hold workflow progress or act as the authoritative copy of your knowledge.
Microsoft’s current guidance for these patterns names Azure Managed Redis. If you run a different Redis offering, confirm that it supports RediSearch and vector search before you adopt the retrieval patterns below.
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When agentic retrieval earns its cost
Fixed RAG and agentic RAG differ in who controls the retrieval path. In fixed RAG, the application receives the query, runs one search, assembles the context, and calls the model. In agentic RAG, search is exposed to the model as a tool. The model requests a retrieval action, the runtime executes it and returns the results, and the model decides whether to search again or answer.
“Standard RAG works well for queries that map to a single search against a single index.” Microsoft Learn, “Develop an agentic RAG solution on Azure.”
| Design question | Fixed RAG | Agentic RAG |
|---|---|---|
| Number of retrieval steps | One, fixed in code | Variable; the model requests each call |
| Source selection | Set at design time | Chosen at runtime across heterogeneous sources |
| Query handling | Used as submitted | Can be decomposed into sub-queries |
| Added cost | Baseline | More model round trips, tokens, and latency; requires stop controls |
| Typical fit | Single index, predictable queries | Multi-step reasoning, changing sources, retrieval mixed with actions |
Choose agentic retrieval when at least one of these holds:
- A single question requires several dependent searches, such as finding a policy and then the exception that modifies it.
- The right index depends on the question and cannot be determined in advance.
- Retrieval results must be evaluated, and the system may need to rephrase and search again.
- Retrieval must be interleaved with actions such as lookups or updates in other systems.
Adding agents does not make a system better by itself. If none of the conditions above applies, extra loops and agents add orchestration and evaluation work without a retrieval benefit. This is an architectural judgment drawn from Microsoft’s description of fixed and iterative flows and their cost controls. It is not a measured result.
Where Azure Functions and Durable Functions fit
Two integrations matter here. They serve different control models, so pick one before you structure the code.
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Use this extension when agent work must survive interruptions or coordinate several agents over time. It persists agent sessions, checkpoints orchestration and workflow progress, recovers after failures, and distributes work across hosts. Two orchestration shapes cover most designs:
Rank #2
- Sequential orchestration when one agent’s output determines the next step. For example, a planning agent rewrites the user’s question, and an answering agent then uses the rewritten query.
- Fan-out/fan-in when independent retrievals can run concurrently and their results must be merged before generation. For example, three index queries run in parallel, and a final agent ranks the combined evidence.
Python agent bindings
The Python agent-bindings extension suits an existing function app. Your function code keeps control of triggers, validation, branching, errors, and responses, while an agent handles one bounded reasoning task. Microsoft’s documentation marks the Python bindings as preview, so pin package versions and check the API surface at implementation time.
- Agent instructions can be stored in an
.agent.mdfile. - The extension creates an Agent for each invocation and closes invocation-owned resources when the function ends.
context.call_agent()schedules the agent operation as a hidden activity. Orchestration replay therefore does not repeat nondeterministic model, tool, or network work.
Choosing between the two
| Aspect | Durable Extension for Agent Framework | Python agent bindings |
|---|---|---|
| Primary role | Persisted sessions, checkpointed workflows, multi-agent coordination | An agent call inside a function app whose code keeps control |
| Who owns control flow | Deterministic orchestration code | Your function code |
| Coordination shapes | Sequential handoffs and fan-out/fan-in | One bounded reasoning task per invocation |
| Release status | Check the current label on the Microsoft Learn page before production | Preview, per Microsoft’s documentation as of 7 October 2026 |
Where Redis fits, and what it should not hold
Microsoft’s material supports several Redis roles. Keep them separate, because each has a different freshness and recovery expectation.
| Responsibility | What it holds | Redis’s role | Governing rule |
|---|---|---|---|
| Durable workflow state | Orchestration history and checkpoints needed to resume a workflow | Not used for progress; Durable Task orchestration records it | Must survive restarts, so cache eviction or TTL expiry must not decide it |
| Conversation and retrieval memory | Selected chat context and searchable recall | Fast, expiring store, or searchable index via a retrieval provider | Treat as context, not as a complete record; expiry follows the TTL |
| Derived cache | Reusable outputs, such as answers matched by vector similarity | Vector-similarity lookup with metadata filters | A miss means recomputation; the freshness policy must be explicit |
Conversation memory with a TTL
Microsoft’s dynamic agents pattern stores conversation context and chat history in Azure Managed Redis. Entries are indexed by conversation ID and expire according to a configurable TTL. Microsoft does not prescribe a key schema. One layout that keeps tenants apart looks like this:
conv:{tenantId}:{conversationId}:turns (TTL set by your session policy)That pattern also uses Azure AI Search vector similarity as a semantic cache for agent selection. That cache is separate from Redis conversation memory, so do not conflate the two in your design.
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Searchable retrieval through TextSearchProvider
The Agent Framework’s provider-independent TextSearchProvider can be backed by Redis search adapters. The integration requires a Redis deployment with RediSearch support, such as Redis Stack or a compatible managed service. Hybrid vector search also requires an embedding provider. The Redis package and its APIs are subject to change, so check current release status before you build on them.
Semantic caching in Azure Managed Redis
Azure Managed Redis supports a semantic-cache pattern built on vector similarity, metadata filtering, and vector indexes. Microsoft suggests custom applications and agents when you need direct control over similarity thresholds, TTLs, partitions, model versions, telemetry, and safety behavior.
Because a cached answer depends on the model that produced it, include the model version in the cache identity. This is a design inference from the model-version control Microsoft lists, not a documented requirement. Keep tenant identifiers in cache keys too, and apply the same isolation rules described in the security section.
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Stream broker for durable streaming
Microsoft’s documented durable streaming pattern uses Redis as a reliable stream broker. Use it to deliver output to clients. Do not treat the stream contents as workflow state, since orchestration history remains the record of progress.
Rank #4
Bound the reasoning loop and keep it replay-safe
Durable orchestrations are replayed from their recorded history, which is why Microsoft describes them as reliable and debuggable. Model calls, tool calls, and network requests must therefore run inside activities or replay-safe framework APIs, not inline in orchestrator code. Then apply these controls:
- Cap tool-call iterations. Microsoft’s agentic RAG guidance (as of 7 October 2026) describes 5 to 10 iterations as a typical cap for limiting runaway cost and latency. Treat that range as a starting point to tune with evaluation, not as an optimum or a benchmark result.
- Define explicit stop conditions: enough evidence has been gathered, the iteration cap is reached, or the loop has stopped converging.
- Track cumulative token use for each request, and stop the loop when a token budget is exceeded.
- Plan the escalation path for non-convergence. Microsoft’s guidance says a loop that fails to converge may need human assistance or a different approach, so return a partial answer with its sources or hand the request to a person.
- Log each iteration with its tool name, query, and result count, so you can trace why the loop ran as it did.
Tune scale and concurrency
On the Consumption and Elastic Premium plans, Durable workers scale according to backlog and latency, and they scale to zero while the task hub is idle. Python and PowerShell apps have runtime concurrency restrictions. If you configure more concurrency than the worker can run, work waits on a single worker. Set concurrency in line with the language runtime, then load test the fan-out path before you raise it.
Measure these values from the start:
- Queue and task wait time
- Activity duration and orchestration replay time
- Per-agent and end-to-end latency
- Retrieval quality, cache hits and misses, token counts, and failures
Set security boundaries around retrieval and state
RAG passes grounding data from a data store through the orchestration layer into model context. Every retrieval path is therefore a potential access path. Microsoft’s RAG description establishes this flow. The controls below are recommendations for your own identity and data model, not prescriptions from Microsoft.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware match- Enforce tenant isolation in the retrieval query, cache keys, memory lookups, and every agent tool. Passing a tenant identifier in the prompt is not an access-control boundary.
- Use managed identities for service-to-service access and keep secrets in Azure Key Vault.
- Where your requirements call for it, use private endpoints for data services and controlled egress for external APIs. Microsoft’s multi-agent architecture illustrates this topology, but it does not make it mandatory.
- Monitor tool calls and retrievals so you can trace which documents reached the model for a given answer.
Evaluate agents individually and as a system
Evaluate each agent on its own, then evaluate the multi-agent system end to end. Repeat both when an agent is added or changed, because a new agent can alter how work is selected and how other agents behave. Microsoft’s dynamic agents pattern shortlists candidates by vector similarity and calls an LLM only when the score is ambiguous. Its 85% confidence threshold appears as an example (“such as 85%”), as of 7 October 2026. It is not a universal recommendation or a validated value. Set your own threshold from evaluation data.
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What drives cost
Azure Functions uses event-driven, pay-per-invocation hosting, but serverless is not automatically the cheapest option. Total cost depends on the plan, the workload, and these drivers:
- Model calls and tokens per loop iteration. Agentic retrieval multiplies these, so the iteration cap is also a cost control.
- Embedding calls for hybrid vector retrieval.
- The Azure Managed Redis tier and capacity, including the search and vector features your pattern requires.
- Storage for durable state, plus telemetry retention.
Microsoft’s pages do not publish a cost estimate for this architecture. Build one from your own token counts, invocation counts, and Redis sizing.
What the published guidance does not cover
Microsoft’s pages do not include a tested end-to-end reference for this exact combination of Functions, Agent Framework, and Redis, and they publish no benchmark for the reasoning loop or the cache. Validate the architecture against your own workload before you rely on any threshold, cap, or TTL in this guide.
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