Table of contents
Highlights
- Evaluating agentic AI platforms now requires a structured framework, not a feature checklist — governance, ITSM compatibility, orchestration depth, and enterprise readiness are the four pillars that separate serious contenders from point solutions.
- The competitive landscape has consolidated around a handful of enterprise-grade platforms — Microsoft Copilot, Google Gemini, ChatGPT Enterprise, and Moveworks — making direct comparison more useful to buyers than a broad survey of tools.
- Orchestration depth is the real differentiator, not raw AI capability. The best platforms plan and execute multi-step workflows across systems, rather than completing isolated tasks within a single app.
- Governance and ITSM compatibility are no longer optional — enterprises need built-in permissioning, audit trails, and native integration with systems like ServiceNow to deploy agentic AI safely at scale.
- Moveworks is purpose-built to unify action across the enterprise, pairing its Reasoning Engine with governance designed for cross-system orchestration — where ecosystem-bound tools like Copilot, Gemini, and ChatGPT Enterprise stop at their own platform's edges.
If your organization is evaluating agentic AI platforms right now, you already know the market looks different than it did even last year. In 2025, “enterprise AI” meant narrow point solutions and automation that felt impressive at the time.
Today, agentic AI solutions are the priority. These platforms let you build and deploy AI agents at scale, and choosing the right vendor is essential. Proven solutions like Microsoft Copilot, Google Gemini, and ChatGPT Enterprise are the default for most evaluations, so a strong comparison uses a framework that’s relevant across all of them.
Plenty of vendors claim to “integrate with your systems” and “orchestrate workflows,” but that doesn’t tell you whether they can actually manage an enterprise deployment. If the solution you choose lacks the controls and visibility needed to complete workflows across systems, integration doesn’t matter.
And with high costs, long implementation timelines, and large companies going all in on AI, the stakes are high. McKinsey's 2025 State of AI report found that 88% of organizations now use AI in at least one business function, but only 39% report EBIT impact at the enterprise level.
If you want to be part of that 39%, it’s critical to choose an enterprise AI platform with the governance, integration depth, and orchestration capability to operate reliably across your entire environment. This guide gives you that framework.
Who this guide is for (and how to use it)
This guide is written for HR and IT leaders who are evaluating agentic AI platforms with the goal of enterprise deployment. This includes heads of HR, CHROs, heads of IT, and CIOs.
Our framework walks you through what to have in place before you contact vendors, and how to evaluate them once you do:
- Define specific use cases and anticipated success metrics before shortlisting vendors.
- Require evidence from vendors when it comes to governance, asking for details about permissions, audit trails, data handling, and documented security certifications.
- Verify integration depth with your existing HR and ITSM tech stacks, and understand exactly what functions come with each.
- Validate workflow orchestration potential and human-in-the-loop controls with a live demo or pilot test.
- Confirm the pricing model, add-on costs, and total cost of ownership for your expected usage.
Agentic AI vs. standard AI vs. autonomous agents
AI is continuously gaining new capabilities, and the distinctions between the types matter when you're making a purchasing decision.
Type | What it does | Key difference | Example |
Standard AI | Follows rules or patterns to complete a single, specific task. May use generative AI and natural language processing or act as a virtual assistant. | Needs a prompt for every action. | A simple spam filter or a chatbot answering basic FAQs. |
Autonomous AI | Works independently from start to finish to achieve one big goal. May use machine learning to improve, and act as a digital coworker. | Works on its own, but stays within a defined boundary (domain). | A self-driving car navigating a route alone. |
Agentic AI | Breaks a complex goal into smaller steps, uses tools, and changes strategy if needed. | Reasons, plans, and carries out entire workflows. | A purpose-built AI tool for employee onboarding that provisions software, shares forms, and sends training reminders. |
Agentic AI platforms are the most capable solution, allowing you to build autonomous AI agents with extensive functionality. But capability without governance is a liability. The rest of this guide focuses on how to evaluate whether a platform can deliver both.
Key capabilities to look for in an agentic AI platform
Agentic AI platforms have a few key capabilities that enterprise organizations need:
- Natural language understanding (NLU): Interprets unstructured employee requests in plain language, which means your employees can ask for what they need conversationally.
- Multi-step reasoning: Capable of planning, sequencing, and executing actions end to end within human-in-the-loop controls you configure, instead of needing to react to prompts.
- Cross-system orchestration: Connects to systems across your tech stack to prevent fragmentation and disjointed workarounds.
- Governance and permissions: Every action is scoped to what the requesting employee is authorized to do, with role-based access controls and audit trails built in.
- Human-in-the-loop controls: Lets you decide exactly where people stay in the approval chain.
- Integration depth: "Connects to" is not the same as natively orchestrating within a system, so verify that the AI platform can take governed actions inside the tools already in your tech stack.
The buyer’s framework: How to evaluate agentic AI platforms for business
The four pillars below turn those capabilities into criteria you can score: governance and oversight, ITSM compatibility, orchestration depth, and enterprise readiness. Use them to assess the platforms on your shortlist.
Governance and oversight
Governance helps create an AI system that takes the correct actions in ways that are explainable, reversible, and secure.
In practice, that demands three things:
- Role-based permissioning that scopes every action to the requesting user's access level
- Audit trails that log what was done, why, and by whom
- Explainability, so admins can understand and challenge AI decisions, correcting them if needed
At this stage, ask vendors:
- Can admins see why a specific action was taken?
- What happens if an agent attempts an action outside a user's permissions?
- How are the platform’s audit logs stored, and can they be exported for compliance review?
ITSM and systems compatibility
ITSM integrations are a must-have for enterprise AI solutions. Your agents need to be able to operate within ServiceNow, Workday, and any other systems of record that you’re using.
Look specifically for native orchestration. “Connecting to” can mean that data simply flows between platforms, while orchestration means the AI can take governed actions like updating records, managing employee or customer support tickets, and triggering approvals inside your existing systems.
Make sure vendors show you those connections during a live demo.
Orchestration depth
Surface-level task completion involves answering a question or drafting an email response. That’s helpful, but it’s not multi-agent orchestration, where a platform handles a multi-step, cross-system workflow end to end.
To test orchestration during a demo, add in a mid-workflow obstacle. For example, ask what happens if a system is unavailable, and watch how the agent handles it.
Ideally, you’d want the agent to find a different path to execution — while still following governance requirements — instead of needing human intervention.
Enterprise readiness (security, scale)
Enterprise organizations work at scale, so security and compliance, performance at volume, and total cost of ownership (TCO) all matter when you’re assessing multi-agent systems.
What to look for in each:
- Security and compliance: Look for baseline certifications like SOC 2 Type II and GDPR-aligned data handling, as well as any industry requirements, such as HIPAA compliance or FedRAMP authorization.
- Scale: Confirm the platform can operate across different locations and departments. Make sure governance controls hold at volume, without performance degradation.
Pricing, procurement, and rollout considerations
All of that matters, but it also has to happen on budget and on time. Three things to watch:
- Pricing and TCO: Agentic AI platforms typically price by usage, seat number, or a combination of the two. Seat-based pricing is predictable, while usage-based pricing can scale well with adoption but sometimes produces cost surprises at volume. Build your TCO estimate around realistic usage levels, and assume that you may need to increase usage over time.
- Procurement red flags: Ask about contract flexibility, workflow configuration ownership, and data portability terms in case you need to migrate to a new platform down the road. Ask specific questions to make sure you understand what you’re signing.
- Pilot before full adoption: Run a time-boxed 4–8 week pilot against a single high-volume use case, with clear success criteria defined up front. IT deflection and HR self-service are good starting points because you can often see impact quickly, and results are easy to measure.
Best AI agents for business: Comparing the leading platforms
Pricing model | Important security certifications | Governance | Unique features | |
Moveworks | Custom quote/ employee/ year | SOC 2 Type II ISO 27001 FedRAMP Moderate Gold Star Certification for CSA Star Level 2 | Role-based permissions audit trails Data residency options | Reasoning Engine for multi-step cross-system orchestration Agent Studio for governed workflow customization AI Agent Marketplace with 800+ pre-built agents |
Microsoft Copilot | $21/user/month (annual) for Microsoft Copilot Business Copilot Studio agents billed separately via Copilot Credits | SOC 2 ISO 27001 FedRAMP High | Strong within Microsoft 365 | Deep Microsoft 365 integration Copilot Studio AI agent builder |
Google Gemini Enterprise | Starts at $21/user/month (seat-based) plus consumption overages beyond quota | Multiple SOC certifications, including SOC 2 ISO 27001 FedRAMP High option available | Governance via Google Workspace admin controls Cross-system governance requires additional tooling | Deep Google Workspace integration Gemini Enterprise Agent Platform for developer-built agents |
ChatGPT Enterprise | Custom quote; reported $45–$75/user/month, 150-seat minimum annual commitment | SOC 2 Type II FedRAMP Moderate HIPAA BAA available | Audit logs per-request Provenance metadata Policy engine for content filtering | Strong general reasoning capability broad plugin marketplace out-of-the-box Salesforce and ServiceNow connectors |
5 questions to ask any vendor
- Can admins see why an action was taken, with audit trail evidence?
- What connectors exist for our ITSM/HR systems out of the box?
- How is data secured in transit and at rest, and what data residency options apply?
- What pricing model applies to our expected volume, and what's the full total cost of ownership?
- How do you support data and configuration export if we need to migrate later?
Why Moveworks leads on governance, orchestration, and enterprise readiness
Plenty of AI platforms are good within their own ecosystem. Copilot excels inside Microsoft 365, for example, and Gemini performs well within Google Workspace. But most tools hit a ceiling at the edge of their vendor stack, and enterprise environments rarely run on a single vendor.
Moveworks is built differently.
The Reasoning Engine goes beyond responding to requests. It is designed to interpret intent, sequence multi-step actions across the right tools in your stack, and adapt processes when conditions change.
Moveworks orchestration spans platforms like ServiceNow, Workday, Okta, and Salesforce, with more than 100 other pre-built connectors available. And critically, governance is built into every action instead of layered on top, which can help keep organizations compliant with federal and industry regulations.
Agent Studio is designed to give companies a governed build environment. Here, teams can customize agentic workflows and develop agents for their specific use cases without rebuilding orchestration logic from scratch. You can use Agent Studio to configure approval gates, permissioning rules, and escalation paths, all of which can help agentic AI stay trustworthy and secure at scale.
Unlike platforms that stop at surfacing information, Moveworks pairs enterprise search with governed action, helping employees get tasks done and automate full workflows. That matters when the alternative is locating the right knowledge article and then navigating three more systems to act on it.
Organizations like Databricks, Broadcom, and Procore have used Moveworks to move from isolated automations to an enterprise-wide AI layer that operates across departments, adapts as processes change, and holds up under the governance requirements regulated industries demand.
Explore the full Moveworks platform and discover what enterprise-grade agentic AI can do for you.
Frequently Asked Questions
Autonomous agents typically perform a specific task within narrow, pre-defined parameters. Agentic AI platforms go further — orchestrating reasoning, planning, and action across multiple enterprise systems to complete complex, cross-functional workflows aligned with business goals.
LLMs give agentic systems their language understanding and contextual reasoning, allowing them to interpret intent and make context-aware decisions. On their own, however, an LLM only reasons — it takes a dedicated reasoning engine and orchestration layer to turn that reasoning into secure, governed action across enterprise systems.
Copilot, Gemini, and ChatGPT Enterprise are strong within their own ecosystems — Microsoft, Google, and OpenAI's respective tool sets — but their orchestration and governance largely stop at those boundaries. Moveworks is built to unify action across all of these tools and more, with a Reasoning Engine and governance layer designed specifically for cross-system enterprise workflows instead of a single vendor stack.
Look beyond time saved to metrics like case deflection rate, resolution speed, and reduction in cross-system manual handoffs. The strongest ROI signal is often qualitative as well: whether the platform reduces the number of tools employees must touch to get work done, since fragmentation itself is a hidden cost.
Start with a small number of high-friction workflows — IT access requests or onboarding are common starting points — and expand gradually once governance and integration patterns are proven. Success depends less on the sophistication of the AI and more on strong data preparation, clear governance, and alignment between business outcomes and platform capabilities.
