Many organisations are actively experimenting with AI agents. There are pilots, demos and impressive prototypes. Yet in production, progress often stalls. Not because AI agents fall short, but because the step from decision-making to execution is insufficiently designed. This is exactly where many AI initiatives get stuck. In an enterprise context, successful AI is not just about intelligent models, but about the architecture around them. Only when the foundation is in place can AI truly scale and accelerate.

Why agentic AI so often remains stuck in pilots

Many AI initiatives start with standalone agents or chat interfaces layered on top of existing APIs. This works well for demos and proof-of-concepts, but rarely for production.

The reason is straightforward. AI agents excel at interpreting information and reasoning, but they are not designed to autonomously act within complex enterprise landscapes. The moment an agent directly executes actions in ERP, CRM or financial systems, legitimate concerns arise around authorisations, audit trails, exception handling and compliance.

Without a clear separation between decision-making and execution, AI quickly becomes difficult to control — and remains stuck in experimentation.

From context to action: why orchestration is essential

In an earlier blog, we explained why Model Context Protocol (MCP) is needed to provide AI agents with context. MCP makes intent, rules and governance explicit: what an agent is allowed to do, when it should escalate, and where accountability lies.

But context alone is not enough. A decision only creates value when it can also be executed reliably and in a controlled manner.

That is why mature AI architectures increasingly show a clear separation into three layers:

• context and intent (MCP)
• decision-making (AI agents)
• execution (enterprise automation)

It is precisely this last layer that is often underestimated.

Workato as the execution layer for agentic AI

In many organisations, that execution layer already exists — in Workato.

Workato is not an AI platform, but an enterprise orchestration and automation platform where critical processes have been running reliably for years. Orders, synchronisations and exceptions are handled through structured workflows, with clear authorisations and full logging.

By using Workato as the execution layer, AI agents do not need to integrate systems themselves, manage credentials or handle exceptions. They operate based on intent: this needs to happen. Workato translates that intent into concrete actions across existing applications, fully aligned with established policies.

This enables autonomy without losing control — and keeps AI scalable, even in regulated enterprise environments.

What agentic collaboration looks like in practice

Business processes rarely consist of a single step. The same applies to agentic AI. In practice, multiple agents collaborate, each with a distinct role.

A common pattern includes:

• one coordinating agent overseeing the process
• specialised agents for specific checks or enrichments
• a central execution layer that performs the actual actions

Take order management as an example. An AI agent evaluates an order, assesses the context and determines the required steps. The actual execution — reserving inventory, activating the order, sending notifications — is handled through Workato. Only when genuine exceptions occur is a human involved, with full context provided.

AI decides. Workato executes.

Why this approach works for enterprise AI

By deliberately separating decision-making from execution, organisations create an architecture that is production-ready:

• processes remain predictable and controllable
• existing automations are reused
• exceptions are explicit and traceable
• governance, security and compliance remain intact

This is what separates an AI pilot from a sustainable enterprise solution.

The role of Ciphix as a Workato partner

Workato delivers execution at scale. Ciphix ensures the architecture is designed correctly.

As a Workato partner, we support organisations with

• defining where AI autonomy makes sense — and where it does not
• connecting MCP, AI agents and Workato automations
• scaling individual AI initiatives into coherent, enterprise-wide ecosystems

Not more automation, but smarter automation — driven by context, intent and controlled execution.

Conclusion: from agentic experimentation to production

Agentic AI only becomes valuable when context, decision-making and execution come together. Without context, there is no direction. Without execution, there is no value.

By combining MCP with a mature execution layer such as Workato, organisations can move from experimentation to reliable enterprise AI — exactly where many pilots stall today.

Curious how agentic AI and Workato fit into your architecture?
Our Workato specialists are happy to explore a concrete, controlled approach that aligns with your processes and governance.