By Inna Weiner, VP of Product, Data & AI, AppsFlyer.
Agentic AI is increasingly being thought of as the next major leap in enterprise productivity. Give an AI agent a goal, connect it to existing tools, and let it analyse, decide, and act. It can lead to faster execution, fewer manual handoffs, and insights that translate instantly into action. What organisation wouldn’t want that? Yet, many are discovering that there is a problem with the infrastructure that sits beneath it. This is clipping the wings of progress.
A stubborn reality
Much of the current conversation around agentic AI focuses on model capabilities such as reasoning, planning, memory, and multi-step decision-making. These are important for sure. But they mask a stubborn reality. Most enterprise systems were never designed for non-human actors to operate them autonomously.
Legacy enterprise tools were built for humans clicking buttons in graphical interfaces, not for agents making structured API calls across platforms. When an AI agent attempts to orchestrate a traditional workflow spanning collaboration tools, CRM systems, BI dashboards, and external platforms, it quickly runs into friction. Authentication models may differ, permissions can be scoped inconsistently, and data schemas rarely align.
Worse, these failures often happen quietly without anyone noticing. An agent does not always “crash” in a way that triggers alerts. Instead, a field maps incorrectly, a permission scope blocks a write operation, or a rate limit is hit outside business hours. The workflow technically completes, but the outcome can be wildly inaccurate. By the time it is noticed, the damage has already propagated downstream.
Jonathan Ydov
The human glue layer
Many companies have unintentionally created a stopgap pattern. AI agents analyse data and propose actions. Yet, humans are still generally responsible for executing those actions across tools. This is the “human glue layer”.
Consider a common scenario. An AI agent detects a performance drop in a marketing channel and recommends shifting 15% of budget to another channel. The insight is correct and timely. But execution still requires a human to log into the advertising platform, apply the change, confirm it, paste the confirmation into a Slack or Teams thread, update a shared spreadsheet, and notify finance. The AI did the thinking, but the human did the doing. And it was anything but instant.
That is not true automation. It is a sophisticated copy-and-paste workflow branded as AI. This pattern exists because many organisations deployed AI assistants before building the action layer they require. They invested in copilots that can summarise, analyse, and recommend, but not in the governance, permissions, and integration infrastructure needed for agents to safely act. The result is that humans become middleware and a constant roadblock.
Far reaching implications
The interoperability gap in agentic AI is real and has far-reaching implications for productivity, governance, and workflow design.
From a productivity perspective, the promise of agentic AI may be to provide insight to action in seconds. But the reality for many organisations today, is insight to action in hours, with a human required to copy data across multiple tabs. Yet, if agents cannot execute end-to-end workflows reliably, productivity gains remain largely theoretical.
Governance is where the stakes become higher. In human-driven workflows, governance is enforced implicitly. A person moving data from one system to another exercises judgement about sensitivity, relevance, and approval. When an agent performs the same action that judgement must be explicitly codified.
Most enterprises today have governance frameworks designed around human behaviour. They were not designed for a world where agents initiate cross-platform actions, move data between systems, or trigger operational changes at machine speed. A rethinking of governance models is needed. Otherwise, organisations will be forced to choose between risk exposure and stalled automation.
Jonathan Ydov
The need for organisational introspection
Perhaps the most overlooked implication of agentic AI is its impact on workflow design. Agentic AI can expose the fact that many existing workflows are fundamentally incompatible with automation. They rely on manual handoffs, UI-dependent steps, undocumented exceptions, and tribal knowledge that is locked in people’s heads.
These workflows may have functioned well enough in the past, but they break down when an agent attempts to execute them. As such, they need to be reimagined as composable, API-native sequences with clearly defined inputs, outputs, approval checkpoints, and fallback behaviours. This requires more than technical integration, it demands organisational introspection. Teams need to ask not just “can an agent do this?” but “should this workflow exist in this form at all?”
Treat Agentic AI as a signal
It may be tempting to view agentic AI as a plug-and-play upgrade to an existing technology stack. Deploy the model, connect a few tools, and reap the rewards. Yet, that mindset is precisely what leads to fragile systems and disappointed stakeholders.
Agentic AI should instead be treated as a signal. A clear indication that an organisation’s stack, governance structures, and workflows need to evolve together. The companies that succeed will be those that invest not only in AI capabilities, but in interoperability to standardise integrations, modernise permissions, clarify workflows, and redefine governance for autonomous execution.
Time to stop relying on the human glue layer. Asking teams to hold together an increasingly complex AI-powered Rube Goldberg machine may work for a while. But it will eventually break at the seams.
