The emergence of the agent economy is fundamentally altering how software is discovered, evaluated, and implemented. Shadow AI risk management has become the defining operational challenge as autonomous AI agents are now actively selecting the tools they use to build products and execute workflows — often without direct human oversight.
For decades, software procurement was a human-centric process: engineers read documentation, managers approved budgets, and procurement teams vetted security compliance. That era is ending. This shift presents a paradox for operations leaders. The efficiency gains are undeniable, yet the risks of "shadow procurement" and ungoverned technical debt are escalating. When an AI agent decides which database to spin up or which email API to integrate, it uses logic that differs vastly from human reasoning. Understanding this new dynamic is critical for maintaining operational sovereignty.
This is a phenomenon closely related to the broader governance crisis around desktop AI agents — where local, autonomous execution brings speed but removes organizational visibility.
The rise of machine-to-machine procurement
Recent observations from the startup ecosystem, particularly within Y Combinator circles, highlight a rapid behavioral shift. Developers and non-technical founders alike are deploying agent swarms — often using tools like Claude Code or OpenClaw — to automate complex development tasks. These agents are not merely writing code; they are making architectural decisions.
The criteria agents use to select vendors are distinct from human criteria. Humans might prioritize brand reputation, sales relationships, or pricing tiers. Agents prioritize parseability. They gravitate toward tools where the documentation is structured in a way that Large Language Models (LLMs) can easily ingest and execute.
The documentation advantage
A prime example of this is the shift in email infrastructure. Legacy providers like SendGrid, despite their market dominance, often have documentation designed for human navigation — sometimes requiring navigation through support portals or complex UI flows. In contrast, newer entrants like Resend are capturing market share because their documentation provides clean, structured code snippets that agents can copy, paste, and execute immediately.
For an operations leader, this means your organization's tech stack might drift away from approved enterprise vendors toward whatever tools your internal agents find easiest to access. If your approved vendor has "contact sales" friction or complex authentication barriers, your autonomous agents will bypass them in favor of frictionless, developer-centric alternatives like Supabase for databases or Resend for communications. This creates a fragmented infrastructure where critical data flows through unvetted channels simply because the agent found the path of least resistance.
Shadow AI risk management: the governance imperative
The industry is currently witnessing a phenomenon described by some as "cyber psychosis" — a state where founders and operators, intoxicated by the speed of AI, run multiple simultaneous agent workflows late into the night. We are seeing scenarios where a single operator might have four or five distinct "conductor" agents running in parallel, building software, researching markets, or processing data streams.
This behavior represents the next evolution of Shadow IT, but at a scale and speed that manual governance cannot match. In traditional Shadow IT, an employee might sign up for a SaaS tool on a corporate card. In Shadow AI, an agent might spin up entirely new infrastructure, deploy code, and transmit proprietary data to third-party APIs within minutes.
The risk is not just financial; it is operational. These "cyber psychotic" bursts of productivity often occur on local machines or personal accounts, completely outside the organization's governed environment. Data sovereignty is lost the moment a high-level executive runs a sensitive workflow through an ungoverned agent on a personal laptop at 3:00 AM. The challenge for the enterprise is to bring this activity in-house — to provide a sovereign infrastructure where these agents can run safely, visibly, and within policy.
For a deeper analysis of these emerging shadow AI agents and their governance risks, the pattern is consistent: speed without structure creates compounding liability.
Swarm intelligence vs. the god model
The prevailing theory of a single "God Intelligence" model is giving way to swarm intelligence. Much like biological systems, the most effective AI implementations are proving to be collections of specialized, lower-cost agents collaborating to solve problems.
We are seeing the emergence of "agent-only" communities where swarms of agents interact, trade information, and simulate complex social dynamics without human intervention. This mirrors how successful operational workflows are built. Rather than asking one expensive model to research, write, and code, effective systems use a swarm: one agent researches, another drafts, a third reviews, and a fourth executes.
The orchestration gap
However, unmanaged swarms create inefficiency. In one documented case, an agent tasked with video transcription defaulted to using Whisper V1 — an older, slower model — simply because it was the first viable option it found. It took an hour to process an hour of video. A human operator later realized that using Groq's infrastructure would have been 200 times faster and significantly cheaper.
This highlights a critical operational necessity: orchestration. Without a "manager" agent or a governed framework to enforce best practices, autonomous agents will optimize for completion rather than performance or cost. They might choose a deprecated API or an expensive processing route because they lack the strategic context a governed system provides. Operations leaders must implement infrastructure that forces agents to utilize approved, high-performance tools rather than defaulting to whatever is easiest to find.
These orchestration challenges are a core dimension of the broader governance challenges facing agentic AI systems across the enterprise.
The liability and identity problem
Perhaps the most significant barrier to the full agent economy is legal standing. Agents are, in legal terms, similar to minors — they cannot sign contracts, they cannot accept liability, and they cannot be sued. Yet, they are executing transactions and making decisions that carry real-world consequences.
Currently, there is a scramble to build infrastructure that bridges this gap. We are seeing the rise of tools designed specifically to give agents email inboxes because standard providers aggressively block bot traffic. But giving an agent an email address does not solve the liability issue.
This is where the concept of the "human wrapper" becomes essential. Organizations need a layer of sovereignty that wraps around the agent swarm. This layer handles the identity, the budget, and the legal liability, while the agents execute the work. You cannot simply let an agent loose on the internet with a credit card; you need a governed environment that acts as the legal entity, setting strict boundaries on what the agent can spend and what contracts it can interact with.
Strategic takeaways for operations leaders
To navigate the agent economy without losing control, operations executives must adopt a proactive stance on AI governance.
1. Audit your documentation exposure
If you are building internal tools for your agents to use, ensure your internal API documentation is optimized for machine reading, not just human reading. If your internal tools are hard to parse, your agents will hallucinate usage patterns or fail to integrate.
2. Centralize agent infrastructure
The "cyber psychosis" of running agents on local laptops is a security nightmare. Move these workflows into a centralized, governed environment. You need visibility into what agents are running, what tools they are selecting, and what data they are accessing.
3. Implement manager agents
Do not rely on single-agent execution for complex tasks. Implement a "manager" layer that reviews the decisions of worker agents. If a worker agent selects a database or an API, the manager agent should cross-reference that choice against an approved vendor list before execution occurs.
The transition to an agent economy is inevitable. The question is not whether agents will start making decisions for your company, but whether those decisions will be made in the shadows or within a framework of sovereign, observable logic. Robust shadow AI risk management — built around visibility, governance, and sovereign infrastructure — is no longer optional for scaling businesses.