AI agent frameworks: comparing the leading options for enterprise applications

Gartner forecasts that 40% of enterprise applications will integrate task-specific AI agents by the end of 2026, up from less than 5% in 2025 (Gartner, August 2025). It also forecasts that more than 40% of agentic AI projects will face cancellation by the end of 2027 because of escalating costs, unclear business value, or inadequate risk controls (Gartner, June 2025). These forecasts highlight both opportunity and execution risk. Framework selection, operating controls, and a clear business case all deserve attention before deployment.

Choosing an enterprise AI agent framework involves two related decisions: how to author the agent’s logic, and how to host, secure, and observe it. AWS, Microsoft, and Google offer managed services that support third-party frameworks. Teams can also supply their own runtime and controls. This guide compares eleven options and suggests a way to test candidates against your workload before committing engineering time.

What "AI agent framework" actually means

"Agent framework" gets used as a catch-all for at least five different things, and comparing them on the same axis produces bad decisions. An enterprise agent stack typically spans five layers.

Start with the simplest design that meets the workload’s needs. Add agents, orchestration, and autonomy when evaluation shows that they improve the outcome enough to justify their additional cost and complexity. A single agent with a few tools may be sufficient for a narrowly scoped process.

The leading frameworks compared

This guide compares eleven frameworks using a 1–5 Liorant editorial assessment of documented features. These scores are shortlisting judgments, not performance measurements or independently validated rankings. Versions, configuration, and integrations affect capabilities. Broad interpretation: 1 = limited support; 2 = substantial integration work; 3 = usable support with trade-offs; 4 = extensive support; 5 = a particular emphasis within this comparison. Ranges indicate integration-dependent assessments; adoption figures do not determine scores.

*Pydantic AI’s orchestration assessment assumes an external durability backend such as Temporal, DBOS, or Prefect. This integration can support recovery after failures; the score does not establish durability parity with other frameworks.

Official documentation for every framework in this comparison

Framework vs. platform: where the agent actually runs

The single most useful pattern to understand in 2026 is that the three major cloud platforms deliberately decoupled "which framework authors the agent" from "which platform runs it." Every one of them hosts agents built in frameworks they didn't write.

Managed services support a range of third-party frameworks, but integration depth differs. Validate hosting support, persistence, security controls, and pricing for the exact framework/service combination. Self-hosting remains an option when your team can operate the required infrastructure and controls.

Many frameworks have free open-source components. Production costs also include inference, engineering and operations, infrastructure, tools, retrieval, observability, and human review. Their relative importance varies by workload and deployment model.

What happened to AutoGen and Semantic Kernel

AutoGen’s repository explicitly identifies it as being in maintenance mode. Microsoft identifies Agent Framework as Semantic Kernel’s production-ready successor and provides migration guidance. These statements do not establish identical support policies for the two older projects.

For a new Microsoft-ecosystem agent project, start your evaluation with MAF. Existing AutoGen and Semantic Kernel deployments need a migration decision based on support requirements, dependencies, and business value. For a task that a conventional function can handle reliably, that may be sufficient.

How to choose: a decision framework by business priority

There is no universal "best" framework — only a best fit for a given dominant priority. Use this table as a starting shortlist, not a final answer.

The most defensible move for a real decision is narrower than this table: shortlist two or three candidates that fit your priority and cloud, then run an identical workload through each as a proof of concept before committing. The step-by-step section below walks through exactly how to structure that PoC.

Security, governance, and the EU AI Act

Governance is an important enterprise selection criterion. In KPMG’s Q1 2026 AI Quarterly Pulse, 57% of respondents reported human validation of outputs without overseeing every agent action or decision. The report also found that 91% of surveyed leaders identified data security, privacy, and risk concerns as the top factor influencing AI strategy over the next six months (KPMG Q1 2026, page 7). Determine whether your intended use falls within the AI Act’s high-risk categories before mapping the applicable duties.

The European Commission’s current implementation timeline places Annex III high-risk obligations at December 2, 2027, and product-related high-risk obligations at August 2, 2028 following the AI Omnibus changes. Map your obligations to the applicable system category and maintain a compliance calendar alongside the technical controls.

Logging, tracing, and human-review capabilities can support a compliance design. Compare those features across your shortlisted frameworks and services, then validate the complete system and operating procedures. The Act does not prescribe or endorse a particular framework.

A second point matters just as much for European buyers evaluating vendor claims: an open-source framework running on your own infrastructure has no automatic compliance posture of its own. GDPR is an obligation about how data is processed; SOC 2 is an attestation about an organization's controls — neither is a property a Python package can claim on your behalf. The chain that actually needs evaluating is longer than most vendor conversations admit.

Place deterministic authorization checks between a proposed action and tool execution. Identity establishes who is acting; access permissions define allowed resources and actions; business rules enforce conditions such as payment limits. Entra-based identity alone does not implement a transaction-amount rule. Enforce that rule in a policy service, gateway, application, or tool before performing the action.

What agents actually cost: the real TCO drivers

Most options offer open-source components, but licensing is only part of the cost assessment. Framework choice can affect engineering effort, orchestration overhead, and operational needs. Measure the full cost of a completed case for your intended architecture.

Additional agent steps, retries, critiques, and larger contexts can increase inference, tool, and review costs. The increase is workload-dependent. Measure it alongside success rate and latency instead of assuming a fixed relationship between autonomy and cost.

Step by step: how to run a framework evaluation PoC

Test a focused shortlist against your own workload. Scope the proof of concept around a representative task, available data, and the controls needed to evaluate it safely; the time required depends on that scope.

1. 01

   Name your dominant priority

   Pick one from the decision table above — control, cloud alignment, prototyping speed, RAG depth, type safety, or TypeScript-native delivery. Trying to optimize for all of them at once is how teams end up over-engineering a simple use case.

2. 02

   Shortlist two or three frameworks, not more

   Base it on that priority and your existing cloud and ecosystem commitments. A wider shortlist multiplies PoC cost without materially improving the decision.

3. 03

   Define one identical workload

   It should reflect a real production case, not a demo. Include at least one multi-step task, one tool call with side effects, and one scenario that should trigger human review.

4. 04

   Instrument the PoC to measure

   Per candidate, capture:

   End-to-end success rate p50 / p95 latency Model & tool call counts Cost per completed case Failure and recovery behavior Tool-authorization security Ease of tracing Deployment complexity Engineering hours spent
5. 05

   Run the identical workload through every candidate

   Same model, same tools, same test cases, same conditions.

6. 06

   Score each candidate against your named priority

   Not against a generic checklist. A framework that "wins" on paper but underperforms on your actual priority is the wrong pick.

7. 07

   Validate the winning combination's security and compliance posture

   Including the full chain from framework to model API to observability to any connected SaaS tool — before committing to production, not after.

This guide does not rely on a universally accepted cross-framework performance benchmark. Public studies compare particular workflows, but their findings depend on tasks, models, and configurations. A controlled evaluation of your own workload provides evidence for your decision.

Frequently asked questions