There is a looming pin that may burst the AI hype bubble in the business world. The issue isn’t that the AI models aren’t capable. It’s the massive gulf between a prototype built in three days and a secure, robust production system. To survive the transition from experimental pilots to everyday industrial and business use, Shawn McAllister, Chief AI Strategy Officer at Solace, argues that we must stop treating AI as a standalone scientific experiment and start treating it as a first-class business citizen of the enterprise IT landscape.
McKinsey estimates that, despite trillions of investment, nearly two-thirds of organisations have not yet scaled their AI projects across the enterprise. Leaders are grappling with this. At the annual World Economic Forum 2026, numerous high-profile CEO sessions were about issues with scaling AI and overcoming the deeply rooted organisational challenges that come with it.
Nice idea – shame it doesn’t scale!
The AI prototype trap is a phenomenon currently haunting enterprise IT. It begins with a successful internal demo – a chatbot that can parse a company’s HR documents or a script that summarizes meeting notes. It’s a “bottom-up” success, where individual departments find tangible uses for AI, which can originally be highly successful.
But these teams are building agents in isolation. A marketing team might build an agent using one open-source framework, while the IT operations team is building another using a different stack. These become bespoke projects – fragile, siloed applications that either don’t interact at all, or rely on point-to-point integrations, usually REST APIs, to function.
While this works for a demo, when leadership attempts to scale these successes into “top-down” enterprise-wide initiatives, they crash into a digital brick wall.
Hurdles holding back agentic AI
There are a number of major barriers that can prevent enterprises from successfully moving their agentic AI projects from experimentation to enterprise production. Some of the most common hurdles relate to access issues, rigid infrastructure, fragmented developments and out-of-date data:
Ungoverned access is built in and opens up the door to vulnerabilities
When agents move from merely reading data to acting on it – executing trades, moving capital, or modifying sensitive customer records – the enterprise attack surface expands exponentially. Without a centralised governance layer, organisations fall into the threat of Shadow AI, where security protocols and access rights are hardcoded into individual agents or ignored altogether.
This can even happen in simple agentic use cases where attacks like prompt injection, if left unchecked, can override guardrails you thought you had in place.
This creates a serious compliance vacuum. If an autonomous agent improperly accesses PII or triggers an unauthorized transaction, the enterprise cannot answer the fundamental question of who, or what, authorized the breach.
It’s hello again to siloed and rigid infrastructure – the enemy of enterprise evolution
Creating AI components such as agents, prompt templates, and vector databases as isolated assets creates a modern version of legacy silos. When AI architecture is built rigidly, it lacks the modularity to adapt to an evolving market. Upgrading an underperforming LLM to a more efficient model becomes a major engineering overhaul rather than a simple configuration change. This results in a new incarnation of “Spaghetti Code”, a brittle web of bespoke dependencies that kills agility and increases long-term technical debt.
Meet the custom-built bottleneck that simply doesn’t pass the repeatable test for industrial usage
The rapid evolution of AI has outpaced organisational standards, leading to a fragmented development landscape. Currently, separate teams tend to adopt quite wildly different technologies and methodologies for every pilot, forcing many new AI projects to become a ground-up “science experiment.”
This lack of a standardized framework makes it impossible to industrialize AI. For ideas to move from the whiteboard to the production floor at enterprise speed, development must shift from bespoke craftsmanship to a repeatable, platform-driven engineering discipline.
A project built with data that is old before it started
To support the dynamic nature of agentic business activities, AI needs the “now,” not the “yesterday.” Most current AI pilots are “hindsight-driven”, relying on static knowledge bases or data loaded once from a snapshot. To demonstrate the value of the use case this is fine but in a production environment, you need up-to-date information to make effective decisions or take appropriate actions. If a logistics agent plans a shipment based on inventory data that is even five minutes old, it isn’t just inaccurate; it’s hallucinating a reality that no longer exists.
Crossing the chasm from experimentation to production
To solve these pain points, enterprises cannot rely on a patchwork of libraries and point solutions. They require a cohesive platform designed specifically for the complexity of the enterprise.
Cue the Agent Mesh, which offers an open agentic AI platform organisations need to effectively build, deploy and operate intelligent and well-governed AI-powered applications – from simple single-agent to powerful multi-agent orchestrated solutions – that interact in real-time with enterprise applications and data.
An Agent Mesh platform, such as Solace’s Agent Mesh Enterprise, can help enterprises move to mission-critical deployment by delivering on a number of critical pillars:
Democratized development
To bridge the gap between initial ideation and functional enterprise systems, organisations must democratize development by lowering the technical barrier to entry. An Agent Mesh facilitates this through a no-code, AI-assisted interface so that business analysts, alongside pro-code options for developers, can ensure that subject matter expertise is directly translated into agent logic.
The whole process needs to be supported by rich, out-of-the-box connectivity to SQL, APIs, and the Model Context Protocol (MCP), which allows agents to link seamlessly with real-time streams and enterprise applications. By providing flexible orchestration that supports both dynamic task-breakdown and prescriptive, compliance-aligned workflows, an Agent Mesh platform enables teams to evolve simple pilots into sophisticated, production-ready systems.
High Performance Orchestration & Data Management
Unlike traditional REST-based chains that can block and fail, an event-driven Agent Mesh allows for asynchronous, parallelized orchestration where multiple agents work simultaneously and recover automatically from individual stalls. To manage the high costs and context limitations of LLMs, an Agent Mesh can employ an intelligent data management capability to pass only the most relevant information to LLMs, thereby reducing “token burn” and preventing hallucinations.
Open deployment
Finally, to navigate the rapidly shifting AI landscape, enterprises must adopt a cloud-agnostic and vendor-neutral strategy to avoid costly lock-in. Utilizing an Agent Mesh with an open-deployment across on-premises, cloud, or hybrid environments ensures that agents, the data they need and context they maintain can satisfy a variety of sovereignty and data security regulations. This flexibility extends to preserving prior investments by allowing the orchestration of third-party, A2A-compliant agents alongside native ones in a single, unified workflow.
A better way to work
What does this look like in practice? When we combine robust engineering with an event-driven Agent Mesh, we see the emergence of use cases across industries that move the needle on agentic AI ROI, and that survive the stresses of production deployment.
Conversational analytics: Democratizing real-time insights
The first hurdle for most enterprises is moving out of their comfort zone of static dashboards. Business users need to query complex systems, such as ERP, CRM, and Inventory, without waiting days for a data analyst’s report for new types of queries. Connecting a one-off agent directly to a database is a security nightmare, and static data is often outdated the moment it’s viewed.
But through a secure, governed interface that meets users where they work (Teams, Slack, Web), a user can run ad hoc queries like, “What are our unit sales and revenue for this morning compared to yesterday?” The Agent Mesh validates the user’s identity, pulls specific real-time data the user is allowed to see, and allows the agent to summarize the answer. The result is dramatic, reducing time-to-knowledge from days to seconds while maintaining strict governance.
Agentic automation: End-to-end autonomy ready for human sign-off
The “holy grail” of AI is the elimination of manual handoffs. This requires automating long-running, multi-step processes such as customer onboarding or credit approvals. Complex workflows are fragile; if Step 3 of a 5-step chain fails, the whole process breaks, requiring manual intervention to fix.
The Agent Mesh manages the “state” of these complex workflows through parallelized orchestration. It can verify identities and check credit scores simultaneously. If one API is slow, it handles the wait asynchronously and moves to the next task in the meantime. If a step fails, it re-tries automatically.
The result is Straight-Through Processing (STP), slashing operational costs and errors while keeping a human in the loop for final approvals.
The bridge to agentic AI production
By adopting an Agent Mesh, agentic AI projects are transformed from isolated experiments into first-class business citizens with the ability to interact seamlessly with legacy enterprise applications, real-time data and people.
AI projects inherit the security and reliability of the existing IT landscape, enabling organisations to tap into the real power of AI projects and deploy their agentic AI use cases across the business.
All of which is developed, deployed and monitored from a common platform that increases re-use and knowledge-sharing so enterprises can get to value quickly.
The era of treating AI as a scientific experiment is finally over.
Shawn McAllister is Chief AI Strategy Officer at Solace Corporation. Shawn is responsible for the strategy and delivery of Solace’s event-driven integration and streaming platform: Solace Platform. He oversees a team of incredibly talented engineers and architects. Shawn works closely with clients to support the adoption of event-driven architecture and to learn first-hand their needs as input to the innovation built into Solace Platform. He has contributed to the development of key OASIS messaging protocol standards, including MQTT 3.1.1, MQTT 5.0, and AMQP1.0.
Before joining Solace, Shawn led software, hardware, and test engineering teams at Newbridge Networks (later Alcatel Canada), where he was responsible for developing features for ATM and Ethernet switches, as well as the 7750 Multiservice IP Router. He holds a Bachelor of Mathematics from the University of Waterloo, with majors in both Computer Science and Combinatorics/Optimization.
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