Choose an LLM agent framework by testing it against the support work your system actually needs to do—not by counting features or assuming one framework is best for every team. First check whether an agent is needed at all: Microsoft’s guidance is to use a conventional function when it can handle the task, and to reserve agents for work that is open-ended, conversational, or requires autonomous tool use. Then compare workflow control, state and recovery, safety and human approval, integrations, operational ownership, and evaluation tools.
For teams assessing options in 2026, the documented candidates covered here include Microsoft Agent Framework, OpenAI’s Agents API and Agents SDK, OpenAI’s Responses API, and LangGraph. They differ in purpose and execution model, so this is an evaluation guide—not a universal ranking. The sources reviewed do not establish a controlled head-to-head benchmark on customer-support workflows.
Decide whether the workflow needs an agent
An agent can be useful when a support request requires conversational interpretation, choosing among tools, adapting to changing information, or coordinating several steps. It adds complexity, however, and should not be the default for every support task. Microsoft’s overview puts the distinction plainly: “If you can write a function to handle the task, do that instead of using an AI agent.” Its guidance favors workflows when steps are defined and explicit execution control matters, and agents when work is open-ended or calls for autonomous tool use. See the Microsoft Agent Framework overview.
For example, a fixed rule that returns an order’s delivery status from an order ID may be adequately handled by a normal function. A request such as “My order is late; can you work out what happened and help me?” may require interpreting context, checking more than one source, and deciding whether to answer or escalate. That is a plausible agent use case, but it still needs defined tool permissions and escalation rules.
#1 Best Overall
- Prefer a function when the inputs, decision logic, and outcome are known and can be handled reliably without model-led choices.
- Prefer a workflow when the process has known stages, branches, or approval gates that the application should control explicitly.
- Consider an agent when the work is conversational or open-ended and the system must select or coordinate tools based on the case.
Build a simple function or workflow baseline for a representative task before comparing agent frameworks. Otherwise, a trial can show which framework handles a task well without answering the more important question: whether agent orchestration was necessary.
Compare frameworks against support requirements
Assess how each candidate fits your architecture and risk boundaries. A feature is relevant only if it solves a real requirement—for example, resuming a case after delayed human approval, or inspecting the tool calls behind an incorrect answer.
| Framework or runtime | Documented fit and capabilities | Execution and state considerations | Pricing in the cited material |
|---|---|---|---|
| Microsoft Agent Framework | Individual agents with tools and MCP servers; functional and graph-based workflows; provider integrations listed for Microsoft Foundry, Anthropic, Azure OpenAI, OpenAI, and Ollama. The overview also covers middleware, telemetry, session state, and human-in-the-loop workflows. | Supports both agent and workflow approaches. Review the overview’s third-party data and permission considerations. The Go framework is identified as public preview on the cited page. | Not stated in the Microsoft overview. |
| OpenAI Agents API | Managed agent runtime option in OpenAI’s comparison of agent approaches. | OpenAI’s documentation compares where it runs, integration effort, state ownership, and tool execution. Check the documented runtime model against your requirements for deployment and data control. | Not stated in the OpenAI Agents guide. |
| OpenAI Agents SDK | Application-run SDK for building agent behavior and integrating it with an application’s runtime. | The application controls deployment, storage, approvals, and runtime integration. That flexibility also means the application team owns these implementation choices. | Not stated in the OpenAI Agents guide. |
| OpenAI Responses API | More direct model integration, presented alongside the managed Agents API and the Agents SDK as a distinct option. | Use the documented comparison to assess how much orchestration and runtime responsibility remains in your application. | Not stated in the OpenAI Agents guide. |
| LangGraph | LangChain’s 2026 landscape article describes it as an agent runtime for complex agents that require precision. LangGraph’s documentation provides its own overview. | The landscape characterization is vendor-authored, not independent evidence that it will perform better on a particular support workflow. Read the LangGraph overview for product details. | Not stated in the cited LangChain 2026 landscape article or LangGraph overview. |
This table summarizes what the cited documentation establishes; it does not imply that these options have identical interfaces, hosting assumptions, or production maturity. Microsoft Agent Framework’s overview was last updated August 25, 2026. OpenAI’s documentation was accessed October 4, 2026. LangChain’s landscape article was published June 6, 2026, and reflects the perspective of a vendor that sells LangGraph-related products.
Evaluate workflow control, state, and recovery
Workflow control
Ask whether the support process needs explicit transitions, branches, loops, or delegation. A case with a known sequence—identify the customer, retrieve an order, check a policy, then either answer or route for review—may be better represented as a workflow than as an agent free to choose every next step. For more open-ended conversations, assess whether the agent can select appropriate tools while still operating within boundaries set by your application.
State across turns and delays
Support interactions can span multiple messages or wait for a human decision. Determine what state persists between turns, where it is stored, which component owns it, and how it is cleaned up. OpenAI’s documentation distinguishes state and execution ownership across its managed Agents API, application-run SDK, and Responses API. Microsoft’s overview documents session-based state and long-running, human-in-the-loop workflows. Compare those documented models with your own retention and recovery requirements.
Interruption and resumption
Test more than an uninterrupted conversation. Interrupt a run while it waits for approval, leave the case pending, and then resume it. Verify that the resumed run has the correct context, that it does not repeat already completed actions, and that a timeout or rejection produces a safe outcome. These are application-level behaviors to test, not guarantees that a framework feature alone supplies a complete support process.
Set safety boundaries for tools and customer-impacting actions
Inventory what the agent can read and change. Retrieving a public help article has different consequences from changing an account or issuing a refund. OpenAI’s guardrails and human-review guidance distinguishes automatic input, output, and tool guardrails from human review before sensitive side effects.
In the documented SDK approval pattern, a tool that requires approval interrupts instead of executing. The application receives resumable state, approves or rejects the action, and resumes the same run. Build and test that boundary for actions such as order cancellation, refunds, account changes, and disclosure of personal data. These examples identify risk classes; they do not mean a framework supplies your organization’s authorization rules.
- Check authorization and arguments near each side-effecting tool, before it changes customer or account state.
- Confirm that a sensitive action pauses before execution, and that rejection cannot accidentally trigger the action through another path.
- Test whether approval resumes the intended run with the right context, rather than starting a duplicate operation.
- Record the decision and action in the audit trail your organization requires.
- Provide a human escalation route for cases the system cannot safely resolve.
Agent-level checks do not automatically cover every tool in a multi-agent workflow. Put validation close to each tool that can create a side effect. Your application remains responsible for permissions, policy enforcement, data boundaries, failure handling, audit records, and escalation. Microsoft likewise tells builders to test their applications and apply suitable quality, security, and safety mitigations, including attention to data flowing to third parties.
Check integrations, data flows, and operating ownership
Make an inventory of the providers, tools, MCP servers, application runtime, and support-system connections the workflow actually needs. Confirm that the candidate documents the integrations you depend on, and test at least one required connection in a small implementation. Microsoft lists multiple model-provider integrations and tool/MCP support in its overview. OpenAI’s comparison distinguishes a managed runtime, an application-run SDK, and more direct API integration; those choices affect how much infrastructure and state handling your team owns.
Map the data and execution path from customer message through model, framework, tools, and any human-review system. Identify which component stores conversation state, which services receive customer data, and who controls permissions and retention. Microsoft’s documentation specifically cautions builders to review third-party data flows and test for quality, reliability, security, and safety.
Language support, integration status, licensing, service terms, and product behavior can change. Confirm those details in the current primary documentation before implementation. The cited material does not establish prices for these framework options, so this guide does not compare their total operating cost.
Run a support-specific evaluation
A useful trial is a small, representative set of support cases run against a fixed model, prompt, tool definitions, and test data. This is a practical method based on official evaluation and approval guidance, not a published benchmark protocol or a claim that one candidate has been tested here.
- Select permitted, representative cases. Include routine information requests, ambiguous requests, a case that should be handed to a person, and at least one sensitive action that must wait for approval. Use only data your team is authorized to use for evaluation.
- Establish the simplest workable baseline. For each case, note whether a conventional function or explicit workflow can meet the requirement. Keep that baseline available when comparing agent-based implementations.
- Implement the same task in each candidate. Keep model, prompt, tool definitions, permissions, and test cases fixed where the compared options allow it. Record any unavoidable difference in runtime or integration setup rather than treating unlike configurations as equivalent.
- Inspect end-to-end traces. Check the response as well as the chosen tools, arguments, handoffs, and approval events. OpenAI documents trace grading and repeatable evaluation runs over datasets in its agent workflow evaluation guide.
- Score each run against explicit criteria. Record whether it resolved the case correctly, chose the right tool and arguments, escalated when appropriate, followed policy, and recovered correctly after interruption. Measure latency and cost only if your team can measure them consistently and independently.
- Rerun after changes. Save the cases and expected criteria, then repeat the evaluation after changing prompts, tools, policies, models, or framework configuration. This helps reveal regressions that a successful single demonstration would miss.
Use your team’s own policy and expected outcomes to define what counts as a correct result; do not treat fluent wording as proof of correct resolution. Trace-level inspection is especially useful when an outcome is wrong, because it can distinguish a mistaken model response from a bad tool choice, a malformed argument, an unsafe handoff, or a workflow-control problem.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choose based on the result, not a universal ranking
No reviewed source provides a neutral, controlled comparison of these frameworks on support workflows, so the evidence does not support naming one as the best overall. LangChain’s 2026 article describes a qualitative review of documentation, repositories, and community feedback, not a controlled support-runtime bake-off. Treat it as vendor perspective rather than proof of comparative support performance.
Rank #4
For an initial shortlist, Microsoft Agent Framework is relevant when its documented agents, workflows, provider integrations, state, and human-in-the-loop capabilities match the architecture under consideration. OpenAI’s managed Agents API, application-run Agents SDK, and Responses API are distinct runtime or integration choices; compare them by execution and state ownership rather than treating them as interchangeable. LangGraph is another runtime to assess for the workflow you intend to build, with its capabilities checked in its own documentation. The deciding evidence should be how each candidate handles your representative cases, approval boundaries, integrations, and operational responsibilities.
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Are agent frameworks customer-support products such as help desks or shared inboxes?
No. The options discussed here are developer frameworks, APIs, or runtimes for building AI behavior. They can be integrated into a support system, but the cited material does not describe them as packaged help desks or shared inboxes.
Does a framework provide the business rules for refunds or account changes?
The cited guidance describes orchestration and approval mechanisms, not your organization’s refund, authorization, or account-change policy. Those rules must be implemented and enforced by the application and its tools.
Is there a published benchmark proving which framework is best for customer support?
The reviewed sources do not provide a neutral, controlled head-to-head benchmark on support workflows. A workload-specific evaluation is needed to determine which option fits a particular system.
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