7 leading companies building agentic AI
Every enterprise software vendor now claims to offer "agentic AI." The term has achieved that special status in B2B marketing where it simultaneously means everything and nothing. But strip away the hype, and you'll find a genuinely useful distinction: agentic AI systems don't just generate content or answer questions. They reason, plan, and execute multi-step workflows with minimal hand-holding.
For operations leaders, this matters. You're not looking for another chatbot that sounds smart but requires a human to actually do anything. You need systems that can validate a document, route it to the right approver, nudge when things stall, and escalate exceptions. That's not generative AI. That's agentic AI.
By 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024, enabling 15% of day-to-day work decisions to be made autonomously, according to Gartner. The question isn't whether to pay attention. It's which vendors actually deliver.
Key takeaways
The agentic AI landscape spans three categories. You'll find foundational model providers (OpenAI, Google), cloud platform players (Microsoft, Salesforce), and purpose-built process orchestration platforms (Kore.ai, Aisera, Moveworks, Moxo). Each serves different enterprise needs.
The real differentiator is how AI and humans work together. The best agentic systems don't replace human judgment. They handle the coordination work around decisions so your team can focus on the calls that actually require expertise.
Evaluation should start with your process, not the vendor's features. Map the workflow you're trying to improve, identify where AI can handle execution versus where humans must stay accountable, then find the vendor whose model matches that reality.
7 agentic AI companies comparison
1. Moxo: Agentic AI for business process orchestration
Most agentic AI tools optimize what gets done. Moxo is focused on how work actually moves.
In real operations, the hard part isn’t generating an answer or triggering an action. It’s coordinating everything around a decision: validating inputs, routing work to the right person, following up when someone stalls, escalating exceptions, and keeping accountability intact when a process spans five teams and three organizations. That “work around the work” is where processes break down.
Moxo embeds AI agents directly inside multi-party workflows to handle that execution layer. AI prepares, validates, routes, nudges, and monitors progress. Humans step in only where judgment is required: approvals, exceptions, risk calls, and commitments.
The system doesn’t replace decision-makers. It makes sure decisions arrive at the right time, with the right context, and don’t disappear into inboxes.
This makes Moxo especially relevant for operations-heavy processes like order-to-cash, procure-to-pay, vendor onboarding, claims handling, and incident or exception management where autonomy without accountability is a liability, not a feature.
"Moxo streamlined our document approvals. We went from days of back-and-forth to same-day decisions." G2 reviewer
2. OpenAI: Foundational models powering agentic AI
OpenAI doesn't sell you agents. It sells you the intelligence that powers them.
Their APIs provide the reasoning, planning, and natural language capabilities that other companies build agentic systems on top of. If you have engineering resources and want to build custom agents tailored to your exact processes,
OpenAI's APIs are powerful raw material. If you need something working by next quarter without a development team, you'll likely use OpenAI's models through another vendor's platform.
3. Microsoft: Integrated enterprise AI
Microsoft's play is integration depth, not AI novelty.
As the dominant enterprise software vendor, Microsoft embeds agentic AI capabilities across Azure, Microsoft 365, and Dynamics through Copilot. The ecosystem advantage is real: when your CRM, email, and cloud infrastructure come from one vendor, AI assistance flows across them without integration headaches.
The limitation is also the ecosystem. Microsoft's agentic capabilities are optimized for Microsoft environments.
4. Google: Enterprise-scale AI with Gemini
Google brings research depth and infrastructure scale to agentic AI.
Their Gemini models and Vertex AI platform offer enterprises tools to build autonomous systems capable of reasoning with context. The data advantage matters for operations dealing with processes that require analyzing large datasets, identifying patterns, and acting on them.
5. Kore.ai: End-to-end agentic platform
Kore.ai built an agentic platform before "agentic" became a buzzword.
Their platform helps enterprises build, deploy, and manage autonomous agents across customer experience, employee experience, and internal processes. Pre-built templates accelerate time to value. Multi-agent orchestration is the differentiator: complex business processes rarely involve a single agent doing a single task.
6. Aisera: Intelligent multi-agent orchestration
Aisera specializes in the messy reality of enterprise service operations.
IT tickets bouncing between teams for weeks. HR requests require five different systems. (Sound familiar?) Governance is baked in, not bolted on. For regulated industries, Aisera includes audit trails, role-based access, and compliance controls as core features.
7. Moveworks: Autonomous workflow agents
Moveworks focuses on a specific promise: resolving requests without human intervention.
Their agents handle support tickets, service requests, and routine operational tasks autonomously. ServiceNow integration is particularly deep. Global enterprises with strict security requirements find Moveworks has invested heavily in multilingual support and compliance.
8. Salesforce: Conversation-driven AI
Salesforce embeds agentic AI where your customer data already lives.
Their approach centers on Agentforce and enhanced AI capabilities within their ecosystem. Cross-system task coordination through Slack created a channel for AI agents to coordinate work across teams. The CRM-centric view is both strength and limitation.
What to look for when evaluating vendors
Start with the process, not the technology. Map the specific workflow you're trying to improve. Where do handoffs break down? Which steps require human judgment versus routine execution?
Integration breadth determines real-world usefulness. An agent is only as capable as the systems it can access. Check connector depth, not just existence.
Evaluate the human-AI boundary carefully. The best agentic systems are explicit about where AI handles execution and where humans retain accountability. Vendors promising fully autonomous everything should raise skepticism.
Why process orchestration matters more than AI capability
Here's the uncomfortable truth: raw AI capability is becoming commoditized. Every vendor has access to similar foundational models. What differentiates outcomes is how that intelligence gets orchestrated across real business processes.
Consider a vendor onboarding process. The AI capability to read a document, extract key terms, and flag missing information has existed for years. What breaks down is everything around that capability: routing the flagged document to the right person, ensuring they review it, handling exceptions, and keeping the process moving without manual chasing.
This is where process orchestration becomes critical. You need a system that coordinates human actions, AI agents, and existing systems within a unified workflow. AI handles the repetitive execution work. Humans handle the decisions that require judgment.
Most automation tools optimize tasks. Process orchestration optimizes responsibility.
Choosing the right one for your business
The agentic AI market is maturing rapidly, but the landscape remains fragmented. Foundational model providers offer powerful capabilities requiring significant assembly. Cloud platform players provide integrated experiences within their ecosystems. Purpose-built orchestration platforms offer faster paths to deployment for specific use cases.
For operations leaders, the evaluation question isn't which vendor has the most impressive AI. It's which vendor's model matches how your processes actually work, where humans must stay accountable, and where AI can genuinely reduce coordination overhead.
The organizations getting real value aren't chasing the most autonomous solution. They're finding the right balance: AI that handles the work around decisions, humans who own the decisions themselves, and orchestration that keeps everything moving.
Ready to explore how process orchestration can transform your operations? Get started with Moxo today
FAQs
What actually makes an AI system "agentic"?
An agentic AI system can reason about goals, plan multi-step actions, execute across systems, and adapt based on outcomes. Traditional AI-powered tools respond to prompts or perform single tasks. Agentic systems pursue objectives with minimal human direction.
How do I evaluate whether a vendor's agentic AI is enterprise-ready?
Look beyond demos. Ask about integration depth, governance capabilities, how the system handles exceptions, and what happens when AI confidence is low. Enterprise readiness means the system degrades gracefully and keeps humans appropriately informed.
What's the difference between agentic AI platforms and traditional workflow automation?
Traditional workflow automation follows predefined rules. Agentic AI can reason about novel situations and decide which path to take based on context. The practical difference: workflow automation handles the expected; agentic AI handles variability intelligently.
How do I start piloting agentic AI without disrupting operations?
Start with a contained, high-volume process with clear success metrics. Choose a process where AI errors have low consequences during learning. Run the agentic system in parallel with existing processes initially, comparing outcomes before full cutover.
