Table of contents
Highlights
- An agentic AI framework provides the structure — communication protocols, reasoning logic, and coordination rules — that lets multiple AI agents plan, act, and collaborate across enterprise systems.
- Enterprises without a shared framework often end up with fragmented agents that work in isolation, creating governance risk, inconsistent employee experiences, and manual handoffs as adoption scales.
- Open-source options like LangGraph, CrewAI, and Microsoft Agent Framework give engineering teams flexibility and control, but require ongoing development, integration, and maintenance to run reliably in production.
- Agentic AI frameworks and agent builders solve different problems. Frameworks define how agents reason and coordinate, while builders make it easier to configure and deploy agents on top of that foundation.
- Moveworks is designed to deliver the framework layer prebuilt, preintegrated, and pregoverned, so IT and HR leaders can deploy multi-agent automation through Tool Studio without staffing an engineering team to build the underlying orchestration and governance themselves.
You've probably already deployed an AI agent or two. One handles IT requests. Another fields HR questions. Within their lanes, they work.
The problem starts when you push for enterprise-wide automation. Agents without a shared foundation can struggle to coordinate, adapt, or operate reliably across systems. When that happens, handoffs often turn manual, and governance exposure can grow.
Those gaps are harder to ignore as AI adoption accelerates. 40% of respondents from large organizations say they're scaling AI agents, up from 27% the prior year.
That coordination problem is what an agentic AI framework is designed to help address. It's also where the build-vs-buy decision gets concrete. You can dedicate engineers to build and maintain the framework layer yourself, or adopt a platform that provides it ready to go.
Below, we'll cover what an agentic AI framework is, what to evaluate, and how to decide which path makes sense for your organization.
What is an agentic AI framework?
An agentic AI framework is the underlying structure of communication protocols, reasoning logic, and coordination rules that lets multiple AI agents plan, act, and collaborate across your enterprise systems.
A framework lets agents share information and coordinate who handles each part of a request. It also supports how they reason through multi-step work and adapt when something changes mid-process, like a missing approval or an updated policy.
For example, during new-hire onboarding, one agent can provision system access while another schedules required training, and the framework helps keep both steps tied to the same overall process.
As your agents spread across departments, that shared foundation becomes harder to do without.
Why agentic AI frameworks matter for enterprise leaders
Many employee requests touch more than one team or system before they're resolved. A shared framework can give your agents a more consistent way to support that work.
These benefits tend to show up first in a few key areas:
- IT provisioning: With the relevant integrations in place, agents can route access requests through the right approvals and account setup, which can reduce the volume of manual tickets and shorten delays.
- HR onboarding and offboarding: Agents can coordinate steps across HR, IT, and facilities, supporting smoother handoffs when someone joins or leaves.
- Employee support: A single request can trigger governed, approved actions across multiple well-integrated systems, giving employees a path to resolution with less waiting on multiple teams.
- Scaling operations: Enterprises can often add new workflows without growing headcount at the same pace.
Without a shared framework, separate agents may each follow their own rules for data access and permissions. That can open governance gaps and lead to inconsistent employee experiences — the kind that compound as adoption grows.
What an enterprise-ready agentic AI framework needs to do
Frameworks vary considerably in what they include out of the box. As you evaluate options, look for these four capabilities:
- Pre-built components: Reusable connectors to the systems your employees already use. Ready-made connectors can reduce custom work each time you add a new integration and make it easier to expand workflows over time.
- Communication protocols: Structured message passing can give agents a consistent, governed way to share tasks, updates, and results, which can help handoffs hold up as a request moves between IT, HR, and finance systems.
- Planning and reasoning: Patterns like ReAct let an agent reason through a step, take an action, and check the result before moving on. With better memory management, agents can carry context from one step to the next as they work through multi-step requests.
- Monitoring and debugging: Tracing can show what an agent did and help you find where a workflow broke down. Real-time evaluations check output quality, while cost tracking helps keep model spending visible as you scale.
Popular agentic AI frameworks
If your engineering team is scoping an in-house solution, these are the options enterprise teams most commonly evaluate:
- LangGraph
- LangChain
- CrewAI
- Microsoft Agent Framework
- OpenAI Agents SDK
- Google Agent Development Kit (ADK)
- Mastra
Each takes a different approach to building and running agents.
Agentic AI frameworks: At-a-glance comparison
Framework | Primary Strength | Best For | Enterprise Suitability |
LangGraph | Graph-based workflows that can pause and resume | Long-running workflows with approval checkpoints | Suited to complex processes that need manual review steps |
LangChain | Prebuilt integrations, with agents built on the LangGraph runtime | Fast assembly with prebuilt integrations | Wide integration coverage can help connect enterprise systems more quickly |
CrewAI | Role-based groups of agents, called "crews" | Delegating tasks across specialized agents | Role structure can map agent work to how business teams already divide responsibilities |
Microsoft Agent Framework | Successor to AutoGen and Semantic Kernel | Teams invested in Microsoft and Azure | Aligns with Microsoft identity, cloud, and monitoring tools you may already run |
OpenAI Agents SDK | Lightweight agents with simple handoffs | Teams building on OpenAI models | Built-in guardrails and tracing offer a starting point for oversight |
Google ADK | Code-first agent development, optimized for Gemini | Teams building on Google Cloud and Gemini | Supports deployment on Google Cloud infrastructure your team may already manage |
Mastra | TypeScript-first agents, workflows, and memory | JavaScript and TypeScript engineering teams | Keeps agent development in the same language as many web applications |
What running these frameworks requires from your team
These frameworks give enterprises the building blocks for custom AI agents. But turning those blocks into something employees rely on every day takes ongoing engineering work.
IT access requests are a useful illustration. Engineers typically connect a framework to your identity and ticketing systems, define what agents can change, and set up a way to track their actions. Once agents go live, your team is responsible for keeping those connections working as models, tools, and policies evolve.
Much of that ongoing effort centers on the agent harness — the overall structure around the AI model, made up of its tools, memory, the loop it runs in, and controls on what it can do. How much of the harness your enterprise builds itself depends on which option you choose:
- LangGraph and Microsoft Agent Framework focus on orchestration, or how agents move through a workflow. They can give engineers detailed control over each step, including human-in-the-loop approvals. Teams often set up monitoring and access controls separately, using tools like LangSmith for LangGraph.
- LangChain and CrewAI offer more prebuilt pieces, which can help teams assemble agents faster. Your team typically still handles hosting, testing, and governance, including memory, so agents can recall context from earlier requests.
- OpenAI Agents SDK and Google ADK come from model providers and can offer a quicker path into those providers' models. Teams often add enterprise integrations and identity controls so agents can work across their systems.
- Mastra is built for TypeScript, a common language for web development, and can suit teams that want to build agents in a language they already know. Many of the same build-and-maintain tasks typically apply.
Each of these options typically needs an internal team to integrate, govern, and maintain the underlying layer.
Agentic AI framework vs. AI agent builder: What's the difference?
AI agent builders come up in the same conversations as agentic AI frameworks. The terms sound similar, but they play different roles.
- An agentic AI framework provides the foundation for multi-agent systems. It defines how agents reason, coordinate, and act.
- An AI agent builder offers higher-level tools on top of that foundation, making it easier for your teams to design, configure, and deploy agents.
Agentic AI framework | AI agent builder | |
Primary purpose | Defines how agents reason, coordinate, and act | Simplifies how agents are created and deployed |
Technical skill required | Engineering and systems design expertise | Ranges from low-code to developer-friendly |
Governance | Built into the framework's architecture | Often available as configurable controls |
Best suited for | Complex, multi-agent systems | Rapid agent development and iteration |
Frameworks and builders are complementary. A builder typically sits on top of a framework and can help teams create and deploy agents more quickly. That's why many enterprises weigh both when deciding how much to build in-house.
How to evaluate an agentic AI framework for your enterprise
The framework you choose can affect how your AI agents handle sensitive data, connect to your systems, and scale with the business.
As you compare options, these criteria can help you weigh the trade-offs:
- Governance and security: Agents often work with employee data and take real actions in your systems. Ask vendors how their solution can support controls like role-based access, audit trails, and human-in-the-loop approval checkpoints.
- Integration and ecosystem fit: Agents can deliver more value when they work inside the tools your teams already use. Look for options with API flexibility, identity system support, and connectivity to ticketing, HR, and collaboration platforms.
- Reasoning and orchestration: Consider whether the option you're evaluating can handle requests that move across several teams. Can agents plan tasks, pass work between one another, and recover if a step fails?
- Scalability and reliability: A setup that runs a few agents well may strain under enterprise-wide volume. Ask about redundancy and how the solution adapts as users, requests, and workflows grow.
- Team skills and operating model: Consider who will build, monitor, and maintain your agents after launch. Some options require dedicated engineers; others give IT and HR teams the tools to configure agents directly.
Your answers can also help clarify the build-vs.-buy question:
- If you're building: Weigh your engineering capacity, observability tooling, state and memory infrastructure, and who owns ongoing maintenance.
- If you're evaluating a pre-built platform: Look at the range of prebuilt integrations and agents, how governance controls are configured, time to production, and fit with your IT service management (ITSM) and HR information systems (HRIS).
Build vs. buy: How Moveworks operationalizes the agentic framework for the enterprise
Open-source options are worth considering for engineering teams that need access to the building blocks to structure the right AI foundation. They offer flexibility when integrating, governing, and maintaining agents over time, but that flexibility comes with ownership.
Your engineers typically take on each integration, policy update, and fix as agents expand across the business. For many organizations, the build-vs.-buy decision comes down to how much of that work you actually want to own long-term.
Moveworks is an agentic AI platform designed to deliver the framework layer prebuilt, preintegrated, and pregoverned. It's built to operate within your enterprise permissions and policies, helping you deploy agents without dedicating a team to build and maintain that foundation from scratch.
With Moveworks Tool Studio, teams can build and govern agents in a structured environment on a foundation built for coordination, security, and observability at enterprise scale. The Moveworks AI Agent Marketplace extends those capabilities with hundreds of pre-built agents for systems like Workday, ServiceNow, and Salesforce.
For enterprises standardized on ServiceNow, ServiceNow EmployeeWorks offers a ServiceNow-native front-door experience powered by the Moveworks Reasoning Engine. It’s designed to help your organization move from early agent deployments toward enterprise-wide automation, without building and maintaining the framework layer in-house.
See how Moveworks Tool Studio can help your enterprise build, coordinate, and govern AI agents.
Frequently Asked Questions
Yes, and many enterprises do — though not always intentionally. It's common for one team to standardize on an orchestration-focused option like LangGraph for a specific workflow while another team adopts a role-based option like CrewAI for a different use case, especially in large organizations where teams operate with some autonomy. Running multiple options can also happen by design, pairing a reasoning and orchestration layer with a separate observability or evaluation tool that works across them.
The trade-off is governance complexity. Each additional option introduces its own configuration, monitoring, and security model, which makes centralized oversight harder unless the organization invests in a consistent governance layer across all of them.
Traditional workflow automation and robotic process automation (RPA) execute predefined, rule-based steps in a fixed sequence. When a condition falls outside what was scripted, the process typically breaks or requires human intervention. Agentic AI frameworks are designed to give AI agents the ability to reason about goals, interpret context, and adjust their actions when conditions change, without requiring every scenario to be explicitly programmed in advance.
In practice, many enterprises use RPA and agentic frameworks together, with RPA handling structured, repetitive tasks and the framework managing more dynamic, judgment-based workflows.
Maintaining an agentic AI framework typically requires a mix of roles. AI or ML engineers build, tune, and update the reasoning and orchestration logic as models and use cases evolve. Platform or DevOps engineers handle deployment, uptime, and infrastructure scaling as agent usage grows.
Security and compliance reviewers are also important, since agents that can take real actions across enterprise systems need ongoing policy review, access audits, and approval workflows. Larger deployments often add a dedicated observability or QA function to monitor agent behavior and catch failures before they affect end users — a meaningfully larger staffing commitment than a single automation script requires.
Data privacy and compliance handling varies significantly by framework and is rarely built in by default. Many open-source options require teams to implement data residency controls, encryption standards, and regional compliance logic (such as GDPR in the E.U.) themselves. This gets more complex in multi-agent systems, where data may pass between several agents and connected enterprise systems, each with its own policies to keep consistent.
Organizations operating across multiple regions typically need to configure where data is processed and stored, restrict which agents can access sensitive information, and maintain audit trails for regional regulators. Treating compliance architecture as a core evaluation criterion early is more practical than retrofitting those controls later.
Agentic AI frameworks aren't exclusive to large enterprises, but the right approach usually differs by company size. Mid-sized organizations often benefit from starting with a narrower scope, such as a single high-volume use case like IT ticket triage or HR question handling. They also tend to have fewer systems to integrate and fewer compliance layers to navigate, which can make evaluation and deployment faster than at a large enterprise.
Mid-sized companies typically have less internal engineering capacity to build and maintain a framework from scratch, which often makes a phased rollout or a pre-built platform approach a more practical starting point.
