An AI agent framework gives developers building blocks for defining agents, tools, and orchestration; a full-stack agent platform adds managed services for running, connecting, securing, observing, or evaluating them. The categories overlap, so choose based on your workload and the parts of the system your team wants to operate—not on a universal ranking. For many tasks, a normal function or explicit workflow is simpler and more predictable than an autonomous agent.
What is the difference between an AI agent framework and an agent platform?
A framework is primarily a development layer: it provides programming abstractions for building agents and coordinating their use of models, tools, and state. A platform is primarily an operational layer: it can provide managed runtime, identity, networking, observability, or evaluation services around an agent application. One product may cover parts of both layers, and a platform may host agents written with several frameworks.
- Framework: You use its code libraries and abstractions to implement agent behavior and workflows. You may still need to choose and assemble hosting, security controls, telemetry, and evaluation.
- Platform: You can use managed services for some operational capabilities, potentially while bringing your own framework. You remain responsible for configuring those services and securing the application.
Microsoft Agent Framework illustrates why the labels are not strict boundaries. Its documentation covers agents, graph-based workflows, tools and MCP servers, state and memory, integrations, security, and hosting-related topics. Microsoft describes it as combining AutoGen abstractions with Semantic Kernel enterprise features and as the successor to both; it also documents migration paths. That breadth does not make it identical to a managed runtime platform.
Amazon Bedrock AgentCore illustrates the complementary platform approach: AWS describes it as a set of managed runtime and lifecycle services that can work with custom agents or agents built using supported frameworks. Its documented capabilities include Runtime, Memory, Gateway, Browser and Code Interpreter tools, Identity, Policy, Observability, and Evaluations. Those are AWS-described service capabilities, not guarantees that an application using them will be secure, reliable, or compliant by default.
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Do you need an AI agent or an explicit workflow?
Use an agent when the task is open-ended enough to benefit from a model choosing among tools or planning steps based on intermediate results. Use a conventional function or explicit workflow when the steps and decision points are known in advance, especially when repeatability, bounded access, or easier testing matters more than autonomy.
Microsoft’s Agent Framework documentation puts the simpler option plainly: “If you can write a function to handle the task, do that instead of using an AI agent.” A workflow can still use models or agents at selected steps; choosing explicit orchestration does not require avoiding AI.
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- Prefer a function for a fixed transformation, lookup, or business rule that can be expressed directly.
- Prefer an explicit workflow when the sequence is known but includes model calls, approvals, retries, or branching that you want to control.
- Consider an agent when the system must select tools or plan among possible actions in response to changing context, and the resulting autonomy can be constrained and evaluated.
Which agent framework should you evaluate?
The following descriptions are the assessments in LangChain’s vendor-authored guide, “The best AI agent frameworks in 2026,” published June 6, 2026. LangChain sells products in this category, so treat these as that publisher’s comparative view—not as an independent ranking or like-for-like benchmark.
| Option | Fit described by LangChain’s guide |
|---|---|
| LangChain | Rapid prototyping. |
| LangGraph | Precise, stateful orchestration. |
| CrewAI | Quick role-based multi-agent prototypes. |
| Microsoft Agent Framework | Teams working in the Microsoft stack. |
| LlamaIndex Workflows | Document-heavy, event-driven pipelines. |
| Google ADK | Teams oriented toward Google Cloud Platform (GCP). |
| OpenAI Agents SDK | Scoped assistants and delegation. |
| Mastra | TypeScript teams. |
AWS also names Strands Agents among the frameworks AgentCore can support. The fit labels above can help narrow what to investigate, but they do not establish that an option is the fastest, cheapest, safest, or most reliable for your application. Confirm current language, runtime, model-provider, and integration support in the relevant official documentation before committing.
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How should you compare frameworks and platforms?
Start with your intended workload and existing stack, then compare the dimensions that affect both implementation and operations. A feature checklist is more useful than a single overall score because orchestration control, managed operations, and security boundaries solve different problems.
| Dimension | Questions to answer |
|---|---|
| Control and orchestration | Can you make paths, tool permissions, and handoffs explicit? How much autonomous planning does the task actually require? |
| State and durability | How are conversation state, persistence, checkpoints, retries, and long-running tasks handled? Which parts are provided by the framework and which require separate services? |
| Developer fit | Does the language, SDK style, and workflow model match your team’s skills and existing application? |
| Models and providers | Which model providers and tool protocols are supported? Are there constraints that matter for your deployment or data requirements? |
| Operations | Are hosting, scaling, tracing, observability, evaluation, and debugging managed together or assembled separately? |
| Security and data boundaries | How are identities, credentials, network access, data handling, and human approvals managed? Where does data travel and persist? |
| Economics | What is metered, what costs money while idle, and how do model, tool, networking, and runtime use affect the bill? |
There is no complete, comparable price calculation or independent benchmark across the named frameworks and platforms here. LangChain’s guide evaluates developer experience during prototyping, production reliability, observability and debugging, ecosystem integrations, and pricing transparency, but its assessments do not establish a universal winner on speed, quality, or cost.
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What does a managed platform add in production?
A platform can reduce the amount of infrastructure a team must assemble, but the benefit depends on which services the application needs and which the team already operates. AWS describes AgentCore as modular and consumption-based. Its FAQ describes runtime choices including serverless microVMs and managed EC2 instances: the microVM option bills active CPU and memory, while instances use underlying EC2 billing plus an AgentCore management fee. These are AWS’s current billing descriptions, not evidence that AgentCore is always cheaper. Model and tool use, idle time, networking, and selected modules also matter to the workload’s economics.
Platform capabilities likewise need to be evaluated as configured components, not as a substitute for application engineering. AWS documents features including VPC connectivity, identity integration, and session isolation. Whether those controls meet a particular application’s needs depends on how they are configured and how the application handles data, tools, and access.
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What must you verify before deploying an agent?
Microsoft cautions builders to account for the terms and costs of third-party servers, agents, code, and models accessed directly outside Azure. Its guidance also asks builders to review what data is shared and received, retention and location, and whether data crosses organizational Azure compliance or geographic boundaries. The builder must implement application-appropriate safeguards and testing, particularly when third-party systems are involved.
Quick Recap
- Test the application’s actual tasks: check expected results, failures, tool selection, retries, and behavior when a tool or model returns an unexpected response.
- Limit access: scope credentials and tool permissions to what the agent needs, and decide where human approval is required before consequential actions.
- Trace data flows: identify what prompts, tool inputs, outputs, and logs contain; where they are sent or stored; and which providers or third parties handle them.
- Plan for recovery: decide how to handle interrupted or repeated work, preserve necessary state, and prevent unsafe duplicate actions.
- Assign operational ownership: identify who monitors failures, reviews evaluations, updates dependencies, and responds to security or service changes.
How do you make a practical selection?
- Define the task and boundaries. Write down what the agent must accomplish, which actions it may take, what data it can access, and where human review is needed. First check whether a function or explicit workflow would suffice.
- Set the non-negotiables. Record language and cloud requirements, model choices, tool integrations, state and recovery needs, latency and concurrency expectations, and data or compliance boundaries.
- Choose what to own. Decide whether your team wants to assemble and operate hosting, identity, telemetry, and evaluation, or use managed services for selected capabilities. A framework can be used without adopting a related hosting or observability service.
- Shortlist based on evidence. Use ecosystem fit and documented capabilities to identify candidates, then verify current support in first-party documentation. Treat vendor comparisons as useful viewpoints rather than neutral tests.
- Evaluate with a representative workload. Test the same tasks, tool access, failure cases, and expected usage against the shortlisted options. Estimate costs using your model calls, runtime patterns, networking, and selected services rather than comparing headline labels.
- Review the production design. Validate access controls, data flows, safeguards, monitoring, evaluation, recovery, and ownership before exposing the system to users or consequential tools.
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