TL;DR
Seven AI agent frameworks now cover almost every enterprise build. LangGraph and CrewAI lead the open-source Python field. Microsoft Agent Framework, OpenAI Agents SDK, Google ADK and Strands Agents are the vendor-backed options. AutoGen is no longer a live choice, because Microsoft moved it to maintenance mode and named Agent Framework its successor. The decision now turns on durable execution, memory, evaluation, and native support for the MCP and A2A protocols. Pick LangGraph for stateful workflows, CrewAI for role-based teams, and your cloud vendor SDK when identity and compliance must stay inside one boundary.
Key Takeaways AutoGen entered maintenance mode in 2026 and Microsoft Agent Framework replaced it, so any 2025 shortlist has a dead entry on it. LangGraph handles branching, checkpointing and mid-run human approval better than any other open-source option. CrewAI gets a role-based agent team running with the least boilerplate, and its Flows layer adds deterministic control. MCP and A2A turned tool access and agent handoffs into open protocols, which removed most custom integration code. Durable execution, tracing and repeatable evaluation are what separate a production framework from a prototype library. The framework matters less than the governance, observability and data layer built around it. AutoGen Has Not Shipped a Release in Almost a Year AutoGen’s last release on PyPI landed on 30 September 2025. Microsoft’s own repository now describes the project as being in maintenance mode and points new users to Microsoft Agent Framework instead.
That one change invalidates most agent framework shortlists written last year. The field also gained three vendor SDKs, two open protocols, and a set of production features that barely existed in mid-2025.
Framework choice in 2026 turns on questions that rarely come up in a demo. Can a run resume after a crash, can a reviewer approve a step mid-flight, and can you swap models without a rewrite. In this article, we’ll cover the seven frameworks worth evaluating, how they compare on production criteria, and how to match one to your use case.
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What an AI Agent Framework Actually Gives You An AI agent framework is essentially the runtime and the set of abstractions that sit between your application code and a language model. It handles the loop where the model reasons, calls a tool, reads the result, and then decides what to do next.
Of course, you can write that loop by hand. But teams that do usually rebuild the same five things within a quarter.
Tool calling and schemas. Turning a Python function into something a model can call reliably, and then validating what comes back.State. Keeping conversation history, intermediate results and scratchpad memory, and doing so across many model calls.Control flow. Branching, retries, loops with a stopping condition, and then parallel steps that fan back in.Multi-agent coordination. Handing work between specialised agents without losing context, and without looping forever.Observability. Traces of every model call, tool call, token count and failure, so that an engineer can actually debug it.The framework is only one layer of a wider stack, which our guide to agentic AI tools maps in full. A framework earns its place when it does those five well enough that your team writes business logic instead of plumbing. It stops earning its place, however, when its abstractions hide something you need to control.
That trade-off is worth naming early. The lightest frameworks give you speed and very little structure, whereas the heaviest give you durability and a steeper learning curve. Most enterprise teams therefore end up wanting the second kind once an agent touches a real system of record.
Do You Need a Framework at All? Microsoft’s own agent documentation puts the test bluntly. If you can write a function to handle the task, write the function instead of reaching for an agent.
So skip the framework when the workflow is deterministic. Skip it too when a single model call with a structured output schema does the job, or when the whole thing is one API call wrapped in a prompt. In those cases a direct SDK call plus your existing job scheduler is cheaper to run and far easier to audit.
Reach for a framework instead when the number of steps is decided at runtime. The same applies when several tools have to be chosen between, or when more than one agent is involved. Our post on AI agents versus chatbots draws the same line from the user-experience side.
What Changed Between 2025 and 2026 Three shifts matter more than any individual release, and each one is still playing out.
1. The Field Consolidated Around Maintained Projects LangChain and LangGraph both reached 1.0, announced on 22 October 2025. LangChain’s release announcement carried a public commitment to no breaking changes until 2.0. CrewAI, meanwhile, crossed its own 1.0 line and now ships releases weekly.
At the same time Microsoft folded AutoGen and Semantic Kernel into a single successor. As a result, projects that stopped shipping quietly fell off enterprise shortlists.
2. Tool Access and Agent Handoffs Became Protocols The Model Context Protocol standardised how an agent reaches a tool or a data source. The Agent2Agent protocol then standardised how one agent delegates to another. It is now maintained under the Linux Foundation, by a steering committee that includes AWS, Cisco, Google, IBM, Microsoft, Salesforce, SAP and ServiceNow.
Above all, both protocols moved integration work out of the framework layer. A tool you expose over MCP now works with any framework that speaks it, which in turn makes framework lock-in far less expensive than it was. Our breakdown of MCP versus A2A covers where each one fits.
3. Durability Became Table Stakes Early agent code lost everything whenever a process died. Checkpointing, resumable runs and long-running workflow support are now standard in the serious frameworks, since enterprise agents run for minutes or hours rather than seconds.
The Seven AI Agent Frameworks Worth Evaluating in 2026 Version numbers and release dates for these AI agent frameworks were checked against PyPI and each project’s repository on 10 September 2026, and not taken from vendor marketing. They move fast, so treat them as a snapshot rather than a specification, and re-check before you commit.
Table 1. The AI agent framework field at a glance, September 2026
Framework Backed by Latest release Languages Built for LangGraph LangChain 1.2.x Python, JavaScript Stateful, branching, long-running workflows CrewAI CrewAI Inc. 1.15.x Python Role-based agent teams with minimal setup Microsoft Agent Framework Microsoft 1.18.x Python, .NET, Go Enterprise multi-agent workflows on Azure OpenAI Agents SDK OpenAI 0.22.x Python, TypeScript Fast builds on a small set of primitives Google ADK Google 2.9.x Python, TypeScript, Go, Java, Kotlin Code-first agents with graph workflows Pydantic AI Pydantic 2.42.x Python Type-safe agents with validated outputs Strands Agents AWS and community 1.55.x Python, TypeScript Model-driven agents with few moving parts
Licensing, at least, is simpler than it looks. LangGraph, CrewAI, Microsoft Agent Framework, OpenAI Agents SDK and Pydantic AI all ship under the MIT licence, while Google ADK and smolagents use Apache 2.0.
The commercial question is rarely the core library. Instead it is the managed platform beside it, so price LangGraph Platform, CrewAI Enterprise or Microsoft Foundry separately before comparing total cost. The framework choice also shapes spend, which our guide to AI agent development cost breaks down.
1. LangGraph LangGraph models an agent as a graph of nodes and edges, together with an explicit shared state object. That design is therefore why it handles complex branching, retries and cycles more predictably than a prompt-and-loop library.
Its four headline capabilities are durable execution, human-in-the-loop interrupts, layered memory, and first-class tracing through LangSmith. Because of that, a crashed run resumes from its last checkpoint rather than starting over.
Choose it when the workflow has real branching, an approval step, or a run that lasts longer than a request timeout. Think twice when the task is a single agent with three tools, because the graph abstraction is overhead you do not need.
LangChain 1.x sits above it as the higher-level agent layer, with a create_agent entry point and a middleware system for concerns like summarisation, redaction and approval. Our LangChain versus LangGraph comparison goes deeper on where the boundary falls, and LangGraph with MCP covers the tool layer.
from langchain.agents import create_agent
from langgraph.checkpoint.postgres import PostgresSaver
from langgraph.types import interrupt
def get_open_invoices(vendor_id: str) -> str:
"""Return open invoices for a vendor."""
return lookup_invoices(vendor_id)
def approve_payment(amount: float) -> str:
"""Pause the run until a human approves the payment."""
decision = interrupt({"action": "approve_payment", "amount": amount})
return "approved" if decision == "yes" else "rejected"
with PostgresSaver.from_conn_string(PG_URI) as checkpointer:
agent = create_agent(
model="openai:gpt-5.5",
tools=[get_open_invoices, approve_payment],
system_prompt="You are an accounts payable assistant.",
checkpointer=checkpointer,
)
config = {"configurable": {"thread_id": "ap-run-4417"}, "recursion_limit": 25}
result = agent.invoke(
{"messages": [{"role": "user", "content": "What is open for vendor A-4417?"}]},
config,
)Three things in that snippet are what make it production code rather than a demo. The PostgresSaver survives a restart, the thread_id identifies which run to resume, and interrupt() pauses execution until a person answers.
The configuration decision that matters in production is the checkpointer. So pass a persistent one such as a Postgres saver instead of the default in-memory store. Give every run a stable thread_id, and set recursion_limit so a looping graph fails fast.
2. CrewAI CrewAI instead organises work around agents with a role, a goal and a backstory, grouped into a crew that executes tasks. That framing maps neatly onto processes people already describe as a team handoff, which is why non-engineers follow a CrewAI design review easily.
The 2026 version has two layers worth separating. Crews give you autonomous collaboration, while Flows in contrast give you event-driven, stateful control over the order things happen in. The official documentation recommends starting with a Flow and calling a Crew where genuine autonomy helps.
Choose it when the problem decomposes into named roles such as researcher, analyst and reviewer. Think twice when you need fine-grained control over every transition, because the role metaphor abstracts some of that away.
from crewai import Agent, Task, Crew
researcher = Agent(
role="Senior Data Researcher",
goal="Find recent, sourced developments on a topic",
backstory="You are a seasoned researcher who checks sources.",
verbose=True,
)
research_task = Task(
description="Research the topic and cite every claim.",
expected_output="A markdown report with clear sections",
agent=researcher,
)
crew = Crew(agents=[researcher], tasks=[research_task], verbose=True)
result = crew.kickoff()Two settings carry most of the production risk. First, set max_iter on every agent so a stuck reasoning loop terminates. Second, set max_rpm on the crew, since otherwise a parallel run trips your provider rate limit halfway through a batch.
3. Microsoft Agent Framework Microsoft Agent Framework is the direct successor to both AutoGen and Semantic Kernel, and it is built by the same teams. Microsoft’s own documentation describes it as combining AutoGen’s agent abstractions with Semantic Kernel’s enterprise features, then adding graph-based workflows on top.
It ships for Python, .NET and Go. Sequential, concurrent, handoff and group-collaboration orchestration patterns all come built in. OpenTelemetry tracing, declarative YAML agents, middleware and Microsoft Foundry deployment are also built in rather than bolted on.
from agent_framework import Agent
from agent_framework.foundry import FoundryChatClient
from azure.identity import AzureCliCredential
agent = Agent(
client=FoundryChatClient(
project_endpoint="https://<your-foundry-project-endpoint>",
model="gpt-5.4-mini",
credential=AzureCliCredential(),
),
name="ClaimsTriageAgent",
instructions="Triage inbound claims and flag anything above threshold.",
)
result = await agent.run("Triage claim 88213.")Migration is the practical question for most teams here. Microsoft publishes separate migration guides from AutoGen and from Semantic Kernel. The concept mapping is close enough that a working AutoGen group chat usually becomes a handoff workflow, rather than a full rewrite.
Choose it when the organisation already runs on Azure and needs agents inside its existing identity, governance and billing boundary. Think twice when your stack is Python-only and cloud-neutral, since some of the value is the .NET and Foundry integration. See our CrewAI versus AutoGen versus Microsoft Agent Framework breakdown for the migration angle, and AutoGen versus LangChain if you are deciding where an existing AutoGen codebase should land.
4. OpenAI Agents SDK The OpenAI Agents SDK is the production version of the Swarm experiment. It deliberately ships a small surface, built on four primitives, just as the official docs set out.
Agents. A model with instructions and tools.Handoffs. One agent delegating to another.Guardrails. Validation on inputs and outputs.Sessions. A persistent memory layer across turns.from agents import Agent, Runner
billing_agent = Agent(
name="Billing Agent",
handoff_description="Handles invoices, refunds and payment status",
instructions="Answer billing questions using the billing tools only.",
)
triage_agent = Agent(
name="Triage Agent",
instructions="Route each request to the right specialist.",
handoffs=[billing_agent],
)
result = await Runner.run(triage_agent, "Why was invoice 4417 rejected?")
print(result.final_output)Built-in tracing makes agent runs visible without extra wiring. It also works with non-OpenAI providers through LiteLLM, even though the tightest integration is still with OpenAI’s own hosted tools.
The setting to get right early is max_turns on the runner. Without it, a pair of agents that can hand off to each other will keep doing so. The cost then shows up on the invoice before it shows up in a dashboard.
Choose it when the team wants a working agent this week and is already committed to OpenAI models. Think twice when model portability is a procurement requirement. Our post on OpenAI AgentKit covers the adjacent no-code layer.
5. Google Agent Development Kit Google’s ADK, similarly, is a code-first toolkit that now spans Python, TypeScript, Go, Java and Kotlin. It combines graph workflows with deterministic code, and it also treats context like source code through automatic filtering, summarisation and token tracking.
Both MCP and A2A are supported natively. The project documentation also describes it as model-agnostic across Gemini, Claude, OpenAI, Ollama and vLLM, and deployment-agnostic across Cloud Run, GKE and custom infrastructure.
Choose it when you want a vendor-maintained framework without a single-model commitment, or when the team is not on Python. Think twice when you need the widest library of third-party integrations, which still favours LangChain.
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Explore Agentic AI Services → 6. Pydantic AI Pydantic AI brings the validation model that Python developers already use for API schemas directly into the agent loop. Outputs are typed and validated, so a class of silent model errors becomes an exception you can catch and then retry.
It supports every major provider, ships its own durable execution and streaming support, and it also integrates with Pydantic Logfire for tracing. Teams that already standardise on Pydantic elsewhere in the codebase therefore pick it up quickly.
Choose it when structured, schema-conformant output matters more than elaborate orchestration. Think twice when you need mature multi-agent patterns out of the box.
7. Strands Agents Strands Agents, finally, takes a model-driven approach, where the model plans and the SDK in turn supplies the lifecycle, tool management and observability around it. Its open-source repository documents first-class MCP support and Python plus TypeScript SDKs.
It runs in your own process with no hosted control plane, and it also supports Amazon Bedrock, Anthropic, OpenAI and Gemini models. So it is a natural fit for AWS-centric teams who want to avoid a heavier orchestration layer.
Choose it when the deployment target is AWS and the agent logic is genuinely model-led. Think twice when the workflow needs explicit, auditable state transitions.
Also Worth Knowing Four more AI agent frameworks come up often enough to be worth a sentence each, besides the seven above.
Semantic Kernel. Still maintained, but Microsoft now positions Agent Framework as its successor. See Semantic Kernel versus LangChain .LlamaIndex Workflows. The strongest option when the agent is fundamentally a retrieval and document problem. Compared in LlamaIndex versus LangChain versus Haystack .smolagents. A deliberately small Hugging Face library for agents that write and run code. Release cadence has slowed through 2026.Mastra. The most complete TypeScript-native option for teams whose product is already a Node application. VoltAgent is the other JavaScript-native contender worth a look.Why No-Code Agent Builders Are Not on This List Visual builders such as n8n, Flowise, Langflow and Dify come up in most AI agent frameworks roundups. They are genuinely useful, so the omission here is deliberate rather than an oversight.
Those products are orchestration user interfaces rather than code frameworks. You assemble an agent by wiring nodes on a canvas, which is faster to start and much harder to version, test and code-review.
So the two categories answer different questions. Pick a visual builder when a business team owns the workflow, and pick a code framework when an engineering team owns it. Our roundup of AI agent builder platforms covers the no-code side properly.
AI Agent Frameworks Compared on Production Criteria Feature checklists age badly. The rows below compare AI agent frameworks on behaviour instead. They hold up better because they describe what each one does when something goes wrong, and that changes far more slowly than the API surface.
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Table 2. How the five most-used frameworks compare on production behaviour
Capability LangGraph CrewAI MS Agent Framework OpenAI Agents SDK Google ADK Durable execution and resume Native checkpointing Via Flows state Native, long-running Session persistence Native Human-in-the-loop Interrupt at any node Task-level approval Built into workflows Guardrail-based Callback-based Long-term memory Built-in store Built-in memory Context providers Sessions Session and memory services Native MCP support Yes Yes Yes, including hosted MCP Yes Yes Native A2A support Via adapter Via adapter Yes Via adapter Yes Model portability Any provider Any provider Any provider Best with OpenAI Any provider Tracing and evaluation LangSmith Built-in plus third party OpenTelemetry Built-in tracing Built-in eval suite Managed deployment path LangGraph Platform CrewAI Enterprise Microsoft Foundry OpenAI platform Agent Runtime, Cloud Run, GKE
Two rows deserve emphasis. Native A2A support matters as soon as agents from different teams or vendors have to talk. A managed deployment path, meanwhile, decides whether your platform team inherits new infrastructure.
Seven Criteria for Evaluating an AI Agent Framework Run every candidate through the same seven questions, and naturally through the same workflow. Answer them with a spike rather than a docs page, since documentation rarely admits a weakness.
1. Durable Execution Kill the process halfway through a run, then restart it. A framework with real checkpointing resumes from the last completed step. A weaker one, by contrast, restarts from zero and then repeats every paid model call.
2. State and Memory Separate working memory inside a single run from long-term memory that persists across sessions. Most frameworks handle the first well, while they differ sharply on the second, and that is where personalisation and account history live.
3. Human in the Loop Any agent that spends money, sends external messages or changes a record therefore needs an approval gate. Check whether a person can inspect and edit agent state mid-run, or only approve a final output.
4. Protocol Support Native MCP means your tools will work with the next framework too. Native A2A means your agent can be called by an agent your team did not build. Both therefore reduce the cost of a future migration, which is the real hedge against picking wrong.
5. Observability You need traces at the level of individual model and tool calls, and with token counts and latency attached. Without them, debugging an agent that failed once in 200 runs quickly turns into guesswork. Our guide to AI agent observability covers what to instrument.
6. Evaluation Ask specifically how the framework supports repeatable scoring against a fixed dataset. Teams that skip this ship regressions every time they change a prompt, and then find out from users. See AI agent evaluation for a working method.
7. Model Portability Price and capability shift every few months, and usually in your favour. So estimate the work to swap the underlying model, then treat any framework where that means a rewrite as a commercial risk. An LLM gateway in front of the framework makes the swap cheaper again.
How to Choose an AI Agent Framework by Use Case Most debates about AI agent frameworks resolve as soon as the use case is stated precisely. The table below maps common enterprise patterns to a default choice and a credible alternative.
Table 3. Framework selection by use case
Use case Default choice Alternative Why Multi-step workflow with approvals LangGraph Microsoft Agent Framework Interrupts and checkpointing are native Research or content team of agents CrewAI Google ADK Role decomposition with little boilerplate Agents inside an Azure estate Microsoft Agent Framework Semantic Kernel Identity, Foundry and .NET support Fast prototype on OpenAI models OpenAI Agents SDK Pydantic AI Fewest primitives to learn Document and retrieval heavy agent LlamaIndex Workflows LangGraph Indexing and retrieval are the core problem Structured data extraction at scale Pydantic AI OpenAI Agents SDK Validated, typed outputs AWS-native deployment Strands Agents Google ADK Bedrock integration, no control plane Multi-vendor agents that must interoperate Google ADK Microsoft Agent Framework Native A2A on both sides
One pattern repeats across enterprise projects. Teams start with the lightest framework, then hit a durability or approval requirement in month two, and finally migrate. So choosing for the second month rather than the first saves the rewrite.
If you are still deciding whether an agent is the right shape at all, agents versus chatbots and agentic AI versus generative AI draw the boundary. For build sequencing, see how to build AI agents and multi-agent workflows .
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Read the Whitepaper → Where Agent Projects Break on the Way to Production The choice of AI agent frameworks explains far fewer failures than most teams expect. Indeed, these five failure points show up regardless of which library is underneath.
Real Data Breaks Brittle Prompts A prototype usually runs on clean sample inputs. Production inputs are different. They carry missing fields, inconsistent formats and edge cases nobody described, so prompts tuned on the clean set degrade quietly.
The fix, therefore, is an evaluation set built from real historical inputs, including the ugly ones, before the pilot ends.
Handoffs Loop and Costs Spiral Two agents that can each call the other will eventually do so, and they will continue until something stops them. So set explicit iteration limits, budget caps per run, and a termination condition that does not depend on the model deciding it is finished. The coordination patterns behind those handoffs are covered in our guide to AI agent orchestration .
Long Runs Lose State A run that takes eleven minutes will eventually meet a timeout, a deploy or a restart. Without checkpointing, all of that work and spend is gone. That is why durable execution moved from a nice feature to a selection criterion.
No Traces Means No Root Cause An agent that fails one time in fifty is almost impossible to debug from logs alone, since the failing path is rarely the logged one. Tracing therefore has to be turned on before the pilot, and not after the first incident. Our post on AI agent challenges covers the operational side in more depth.
Governance Arrives Late Data access, PII handling, model approval and audit logging are usually raised by a risk team only after a working demo exists. Bringing agentic AI governance into the design phase avoids a rebuild, and agentic AI risks lists what reviewers will ask about.
Governing Agent Frameworks, Access Control and Permissions Here is the position that runs through every Kanerika agent engagement. The framework is the least consequential choice you will make. What decides whether an agent is safe to deploy is the governed data and tool surface underneath it.
Two agents built on different frameworks, once pointed at the same permissioned data layer, behave about the same. Two agents built on the same framework, by contrast, one with row-level permissions and one without, behave nothing alike.
None of the seven frameworks enforces your access model for you. Each gives an agent whatever credentials you hand it. So an agent inherits the permissions of the identity it runs as, and not those of the person who asked.
The Permission Question Nobody Asks Until the Security Review Most first builds run the agent under a single service account with broad read access, usually because that is the quickest path.
That works in a pilot. But it fails the moment two users with different entitlements ask the same question. The agent will answer both from data only one of them should see.
Three patterns solve it, listed here in ascending order of effort.
Identity pass-through. The agent calls downstream systems as the end user, so existing entitlements apply unchanged. Cleanest option, and the reason Microsoft Agent Framework appeals to teams already on Entra ID.Filtered retrieval. The agent queries a governed layer that applies row-level and column-level security before results reach the model. This is where a catalogue such as Microsoft Purview or Unity Catalog earns its keep.Tool-level authorisation. Each MCP tool checks the caller’s entitlement itself and refuses out-of-scope calls. Most portable across frameworks, and the most code to maintain.Certainly pick one deliberately at design time. Otherwise, retrofitting any of the three onto a working agent means re-testing every prompt and every tool, since the data the model sees has changed.
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Take the Assessment → Audit Trails Are a Framework Selection Criterion A reviewer will eventually ask what the agent did, on whose behalf, with which data, and who approved it. Answering that needs the trace to carry the calling identity alongside the model and tool calls. However, that is an instrumentation property rather than something you get for free.
Frameworks with OpenTelemetry output make this straightforward, since the traces land in the same observability stack your other services already use. Our agentic AI governance guide and AI governance practice cover the control set in full.
How Errors and Exceptions Actually Get Handled Agent failures generally split into three kinds, and each one needs a different response.
Tool failures. A timeout or a 500 from a downstream system. Retry with backoff, and then surface the failure to the model as a structured error, so it can choose an alternative rather than hallucinating a result.Validation failures. The model returned something that does not match the expected schema. Retry once with the validation error appended, which is exactly the loop Pydantic AI formalises, then fail to a human.Reasoning failures. The agent loops, oscillates between two tools, or declares success without doing the work. Only iteration caps, budget ceilings and an evaluation suite catch these, because no exception is ever raised.The third kind is the one that reaches production, precisely because it never looks like an error in any log. So treat a run that finished without an exception as unverified until an evaluation scores its output.
Vertical Requirements Change the Shortlist Regulated industries, by contrast, narrow the field quickly. Banking and insurance work usually needs identity pass-through, full audit trails and a deployment target inside an existing compliance boundary. That in turn favours Microsoft Agent Framework or a self-hosted LangGraph.
Marketing, customer support and internal research teams, meanwhile, have far more latitude, and the lighter frameworks win on speed to first value. Our sector-specific breakdowns cover agentic AI in banking , agents for customer support and agentic AI in supply chain .
How Kanerika Builds Production Agent Systems Kanerika is an AI-first data and automation consulting firm, founded in 2015 and headquartered in Austin, Texas. Delivery teams sit across the United States, India, Argentina and Singapore. Agent work sits inside a broader data and AI practice, and that shapes how we approach framework selection.
How Kanerika Sequences an Agent Engagement The sequence we use on agent engagements has four stages, and each one has an exit condition rather than a deadline.
Assess. First, map the decision the agent will make, the systems it must touch, and the approval points a risk team will require. Because most projects lose more time here than in engineering, we front-load it.Design. Choose the framework against the seven criteria above, then design the data and retrieval layer underneath it. An agent is only as good as the context it can reach. That is why ontology-driven grounding and agentic RAG come before prompt tuning.Build and evaluate. Then ship a narrow agent with tracing and an evaluation set from day one. Then every prompt or model change is scored against the same fixed dataset before it reaches users.Govern and scale. Finally, add access controls, PII handling, audit logging and cost guardrails, and only then widen scope. Our AI governance practice owns this stage.The Same Pattern in Our Own Products Our own product line is built on the same pattern. Karl analyses enterprise data and answers business questions directly, delivering 65 percent time savings on data analysis and a 78 percent increase in team efficiency. Klara meanwhile checks documents against a compliance playbook and suggests redlines, while still keeping a human reviewer in the loop.
What This Looks Like on a Real Engagement One recent engagement makes the architecture concrete. A global expert-network business could not reliably match niche survey requests to the right specialist across three disconnected systems. That produced mismatched experts, rework and a heavy support load.
Kanerika built a context-aware agent that used semantic search across skills, domains and expertise levels, then validated each shortlist automatically against past participation and compliance data. The measured outcome was an 80 percent decrease in mismatch tickets and a 40 percent increase in mapping accuracy. The survey lifecycle also shortened by 34 percent, with 22 percent bandwidth savings for the internal team.
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Read the Case Study → Credentials That Shape the Design That engagement is also a fair test of this article’s argument. The winning decision was not which of the AI agent frameworks to use, it was building a governed retrieval layer the agent could trust before any orchestration code was written.
Work like that runs inside a compliance boundary, which is why our credentials matter to the design rather than only to procurement. Kanerika is a Microsoft Solutions Partner for Data and AI with the Analytics specialisation, a Databricks Consulting Partner and a Snowflake Select Tier partner.
We hold ISO 27001, ISO 27701 and ISO 9001 certifications, are SOC 2 Type II compliant and CMMI Level 3 appraised. Everest Group named Kanerika a Major Contender in its 2026 Microsoft Azure Services PEAK Matrix assessment.
That combination is what lets an agent touch regulated data without a six-month security review at the end of the project. Teams evaluating build partners can compare approaches in our roundup of AI agent development companies .
Wrapping Up The 2026 field is smaller and steadier than the 2025 one, all in all. Seven AI agent frameworks cover almost every enterprise pattern, while the protocol layer underneath them is now shared, and the projects that stopped shipping have removed themselves from consideration.
Run your shortlist of AI agent frameworks through the seven criteria with a real spike, rather than with a feature table. Durability, approval gates, tracing and evaluation will, in practice, tell you more in two days than a month of comparison reading.
Then check the answer against the permission model you actually have. After all, an agent that cannot respect an entitlement boundary is not deployable, whichever framework produced it.
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What are AI agent frameworks?
What are the 5 types of agents in AI? AI agents are often categorized into five types based on complexity and capability:
Each type handles perception, decision-making, and action differently, with increasing levels of intelligence.
Is ChatGPT an AI agent? Not exactly. ChatGPT is a large language model, not a complete agent on its own. However, it can act like an AI agent when wrapped in a framework that gives it tools, memory, goals, or task handling capability.
What are level 3 AI agents? Level 3 AI agents are agents that can act autonomously, use external tools or APIs, and complete multi-step tasks without constant human intervention. They often manage memory, retry logic, and task planning and can collaborate with other agents or systems.
What is the best framework for agents? There’s no one-size-fits-all answer.
The best choice depends on your project’s complexity and goals.
Which is better, CrewAI or AutoGen? CrewAI is better for fast, role-based teamwork with simple setup. AutoGen is stronger for multi-agent conversations, advanced workflows, and tasks like code generation. The right one depends on your need: CrewAI for speed and clarity, AutoGen for structure and depth.
Is AutoGen owned by Microsoft? Yes. AutoGen is developed and maintained by Microsoft Research. It is open source and intended for building multi-agent conversational AI systems, especially where dialogue and event-based communication are central.
Can CrewAI execute code? Yes, CrewAI can execute code if the agent is given the right tools and permissions. Developers can define agents that interact with APIs, scripts, or external systems, including code execution tasks such as analysis, testing, or validation.
Is AutoGen free to use? Yes, AutoGen is open source and free to use. It is available under the MIT license, meaning it can be used, modified, and extended for both personal and commercial projects.
Is CrewAI open source? Yes. CrewAI is open source and publicly available. It’s built in Python and is actively maintained, allowing developers to contribute, customize agents, and extend its use for different business or personal workflows.
