Industry executives and experts share their predictions for 2026. Read them in this 18th annual VMblog.com series exclusive.
By Matt Fitzpatrick, CEO of Invisible
Right now, autonomous agents promise a future where AI plans, decides, and acts on our behalf. Yet in most large organizations, that future feels out of reach. This is because the environment it’s dropped into isn’t built for reasoning.
Early deployments reveal a pattern. When agents are asked to operate across fragmented data, they become interpreters, burning cycles reconciling inconsistencies rather than producing insight. Autonomy exposes organizational complexity.
Most enterprises trying to “go agentic” are discovering a painful truth: the limiting factor isn’t models, it’s plumbing.
On paper, autonomous agents sound straightforward: wire an LLM into your tools, give it a goal, and let it execute. In reality, most organizations still lack the unified data environments and process documentation required for agents to reason effectively. The data estate is a patchwork of SaaS silos, legacy systems, and un-versioned spreadsheets. Processes live in people’s heads, half-written SOPs, or Slack threads.
Drop agents into that environment and they don’t become smart coworkers; they become very expensive interns, spending most of their “thinking time” just trying to interpret inconsistent inputs. Instead of compounding performance, they amplify noise.
The first missing piece is a usable data layer: an environment where customer, transaction, operational, and content data are cleaned, structured, and accessible in consistent schemas with clear provenance. That doesn’t mean boiling the ocean into a single warehouse, but it does mean deciding what “source of truth” actually means for core entities, and instrumenting the paths that matter. Until you do that, every autonomous workflow degenerates into special cases and brittle glue code.
The second barrier is orchestration. A single agent doing a narrow task in isolation is easy; coordinating multiple agents across departments is not. Real enterprise work cuts across finance, operations, support, sales, and compliance. That demands systems that can monitor, evaluate, and correct agent behavior in something close to real time. You need different agents looking at different data sources and perspectives, agents talking to each other and comparing their findings, and agents that specialize in quality control-spotting inconsistencies, challenging weak recommendations, and selecting the best response before anything hits a customer or a system of record. The real capability in 2026 isn’t logging what a single agent did; it’s orchestrating a mesh of agents that can cross-check, veto, and improve each other’s work.
Then there’s governance, which will slow progress more than most vendors admit. You’ll need infrastructure guardrails that control what agents are allowed to do, such as blocking certain RAG or MCP calls, or constraining which systems they can touch. Couple that with real-time evals that watch what agents say and return, catching policy violations, sensitive data, or unsafe behaviour before it reaches a customer or a system of record.
As agents gain autonomy-touching money, customer accounts, or sensitive records-organizations will need frameworks for traceability, approval, and recovery when things go wrong. It’s not enough to log prompts and responses.
You need:
- Clear operational boundaries: what an agent is allowed to touch.
- Action-level audit trails that a non-ML auditor can follow.
- Rollback and “big red button” scenarios that don’t rely on the one engineer who understands the system.
Ironically, this kind of governance is easiest when you’ve already done the unglamorous work of process mapping and data alignment. If you don’t know how the human process works today, you won’t be able to explain or control the agentic one tomorrow.
Bottom line: the next leap in agentic capability will come from infrastructure machines can actually understand.
As we look ahead to 2026, it’s becoming increasingly clear that success with agentic AI will belong to the organizations that treat data clarity and governance as prerequisites. The future of autonomy is about building environments where intelligence can finally compound.
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ABOUT THE AUTHOR
Matt Fitzpatrick is CEO of Invisible. Beyond running a company that operationalizes AI for some of the world’s largest organizations, Matt brings a rare POV shaped by his years at McKinsey, where he advised enterprises on the exact transformation challenges they’re now confronting with AI.
