Why Most AI “Agents” Don’t Work in the Enterprise and How to Fill the Gap

Software is known for being good at repeatable tasks, but most real knowledge work isn’t 100% repeatable; it’s messy, cross-functional, and requires human judgment and expertise.

The promise of AI is a revolution in how this kind of work gets done; every SaaS provider says that they provide it, and every enterprise says they want it. But the reality is that too many tasks remain out of reach for humans to be fully out of the loop. AI is already helpful as it exists today, but not yet accurate or reliable enough to handle the complexity of real workflows on its own.

Agentic AI was built to close that gap, equipped with deep reasoning, action and tool-calling capabilities, and multi-system support. But even now, very few agents run in production at large, impactful organizations. Why? Because most of the agents available today make for great demos, or can be helpful in less critical contexts like summarizing meeting notes, but they still lack the reliability or control that enterprises need for their high-stakes operations.
‍

Key Challenges in Enterprise AI Agents

These are a few of the key challenges of getting AI agents to actually work in the enterprise:

1. Security and governance - Enterprise data is sensitive and regulated, requiring strict controls on access and use. Regulated Enterprises performing tasks like KYC or KYB often need to be able to rigorously demonstrate that the new process improves on the old on both cost and performance.

2. Multi-system orchestration - Enterprise workflows span multiple systems, each with their own data structures, permissions, and interfaces.

3. Workflow flexibility - Business processes are constantly evolving as organizations evolve due to strategy / priority shifts, reorganizations, and operational improvements.

4. Scale and performance. Enterprise solutions must handle millions of data points, support concurrent users globally, while delivering on high speed and performance .

5. Domain expertise - High-value workflows depend on specialized knowledge that lives in the heads of subject matter experts, such as the assumptions, how decisions get made and why, and how to handle edge cases, none of which are necessarily written down.

The question, then, is: what would it take for an agent to survive in the chaos of real enterprise workflows? 
‍

How Composability Unlocks the Long-Tail of Enterprise AI Use Cases

The solution is an agent-building platform designed to fit into the messy realities of evolving workflows, meaning, enterprise agents must be highly configurable, composable, and modular—able to deploy into existing workflows, adapt with changing needs of the organization, and be configured by domain experts without code.

In practice, that means agents must have the ability to call upon or use:

• Data integrations with the underlying permissions respected

• Pre-defined, canonical document collections

• Custom, enterprise-specific actions (such as in our open source actions repository here)

• Other specialist agents that call each together for a workflow

• Reason with deep thinking mode

• Bulk queries across a large corpus of documents or questions

• Humans for approval for high-stakes actions

• Existing UIs and platforms that knowledge workers already use

• A semantic cache translating user intent to a sequence of parameterized actions 

• An API accessible audit trail of everything that AI is doing, when, how, and why

• Asynchronous updates for long running tasks

Credal is the only platform we know of that supports all of these at enterprise scale, making it possible to build cross-functional agents that actually work for multi-step, sophisticated workflows, the ones that make major business impact.

And now, agents built on Credal can run wherever teams already work: Slack, ChatGPT (via custom GPTs), Cursor, Claude Desktop, Windsurf (via MCPs), Google AgentSpace (via A2A), and more, with no separate UI needed.

The next frontier for enterprise AI will go far beyond “agents that do things” - these agents will offer the highest level of composability that rewires how the enterprise works as a whole. In this new model:

• A playbook that once lived in a subject-matter expert’s head becomes transferable, automated, and no longer the bottleneck
• Value compounds because each new action or agent joins a shared library across the organization, and so subsequent use cases get easier and easier to create
• Every query, decision, or outbound change is permission-checked, versioned, and auditable. Learn how we do this in our most recent post on Action Release Gates→

Credal already provides a governed, composable agent-building platform built for your highest-value, most complex workflows. If you’re interested in learning more about how Credal can power your team in this way, feel free to contact us here or reach out at sales@credal.ai.