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That is the promise, at least. Across the technology industry, companies are racing to build AI systems that can retrieve information, reason across sources, use tools, take actions, and complete work end to end. The ambition is clear. The potential is real. The demos are impressive.
But there is still a gap between what agents can appear to do in a controlled environment and what enterprises can trust them to do in production.
That gap is not just intelligence. It is trust.
A model can be fluent without being informed. It can be confident without being correct. It can produce an answer without making clear how it got there, why it is right, or whether it was shaped by the right expertise, source material, and permissions.
For consumer use cases, that risk may be tolerable. For enterprise work, especially in legal, tax, audit, compliance, risk, finance, and regulatory environments, it is not. In these settings, “almost right” is not good enough.
The uncomfortable truth for enterprises is that AI accountability cannot be outsourced to the vendor that sold you the agent. AI may accelerate the work, but the professional and the enterprise still own the judgment, the advice, and the outcome.
That is why the next phase of enterprise AI will not be won only by the company with the most powerful model. It will be won by the companies that can connect AI to the trusted systems, authoritative content, workflows, permissions, and human oversight that real work requires.
We are moving from an era where software helped professionals do the work to an era where AI will increasingly start the work. In high-stakes environments, that only works if the system can stand behind the work with trusted content, expert-built workflows, transparent reasoning, and human verification.
That changes the competitive landscape for enterprise software.
For years, software value was often measured by workflow ownership. Systems of record, systems of engagement, and vertical applications became valuable because they sat close to the work. They housed the data, shaped the process, and became embedded in how professionals operated.
Agents will change that. As AI begins to move across systems, take actions, and assemble work on behalf of users, the value of software will shift from simply owning the interface to governing the work itself.
That is a much higher bar.
In professional environments, context is not just more data. For a lawyer, it may include the controlling authority, the procedural posture of a matter, the relevant client documents, jurisdiction, deadlines, prior work product, and the specific task at hand. For a tax professional, it may include entity structure, regulatory requirements, transaction history, reporting obligations, and client-specific positions. For a risk or compliance leader, it may include policy, precedent, audit trails, approvals, and jurisdiction-specific obligations.
An agent that does not understand that context can still sound persuasive. That is precisely the problem.
This is where much of the current agent conversation misses the mark. More autonomy increases the need for more trust, not less. The more consequential the task, the more the system must be able to show what it used, what it did, why it produced a result, and how a human can verify it.
Protocols such as the Model Context Protocol, or MCP, point to an important shift in the market. Agentic systems need standardized ways to access approved tools, systems, and context. Without that, enterprises are left with brittle one-off integrations, copied-and-pasted prompts, limited retrieval, and unclear permissions.
That matters. But access alone is not enough.
Trusted context is necessary, but it is not sufficient. High-stakes professional work requires something more: AI built to the standard of the professionals who rely on it.
At Thomson Reuters, it’s called Fiduciary-Grade AI: AI built for professionals operating under duties of care, regulatory oversight, and real accountability.
That is the premise behind the next generation of CoCounsel Legal. It is not simply a model pointed at legal content, and it is not just a data access layer. Built to a fiduciary-grade standard, it brings together authoritative legal content, professional workflow tools, domain-specific AI capabilities, rigorous privacy and security safeguards, permissioning, and transparent outputs designed for human review and verification.
This is not just a legal technology issue. It is the central enterprise AI challenge: how to make agentic systems capable of enabling human accountability for consequential work.
Every executive deploying AI into consequential workflows will face the same questions. What sources did the system rely on? Was the output grounded in approved information? Did it respect the right permissions? Can a human verify the work? Can the organization explain what happened if the result is challenged?
Those questions will matter more as agents become more capable.
The first wave of generative AI rewarded fluency. The next wave will reward accountability. Enterprises will not measure agents by whether they can produce a polished answer in a demo.
They will measure them by whether the work can be trusted, verified, governed, and used. That is where durable software value will live.
Agents will become more capable. Standards will mature. Protocols will make integrations more portable. Enterprises will push AI deeper into workflows that were previously too complex or too risky to automate.
But the fundamental test will remain the same: Can the work be verified and trusted?
Anything less may be good enough for a demo. It will not be good enough for production.
That is the future Thomson Reuters is building toward: AI that does not just start the work but helps professionals and enterprises stand behind it.
This article has been written and paid for by Thomson Reuters. The content is not WIRED editorial content. The content does not necessarily reflect the views of WIRED, its affiliates, or owner, and does not reflect any endorsement direct or indirect of Thomson Reuters, its affiliates, or other clients.
