Governing agentic AI in legal practice

For the better part of two years, the legal profession has watched the rapid evolution of artificial intelligence with a mixture of curiosity and caution. First came the fascination with large language models (LLMs) and their ability to summarise cases, draft correspondence, or surface relevant precedents in seconds, often transforming tasks that once consumed hours.

But the conversation is shifting. As AI agents begin to operate not merely as drafting tools but as semi autonomous actors within legal workflows, the question facing firms and in house teams is no longer simply ‘what can AI do’ – it’s how to govern what it does.

As these systems “join the workforce,” the legal sector must confront a challenge that is both operational and ethical: how do you supervise a digital colleague whose output may carry legal consequences? The answer will shape not only how firms adopt AI, but how they protect their clients, their professional obligations, and their own liability.

The evolution of AI in the legal sphere

To understand the urgency of this moment, it helps to look at how the industry arrived here. The early phase of AI adoption in law was dominated by the capabilities of LLMs. Firms experimented with tools that could summarise a collection of documents, extract clauses, or generate first draft contracts.

As models have matured, attention has shifted from raw capability to workflow integration. The question is no longer whether an AI system can produce a coherent draft, but whether it can reliably execute a multi step task inside a legal process and do so consistently enough to be trusted at scale.

New standards such as the Model Context Protocol (MCP) have accelerated this shift by allowing AI systems to communicate with one another and with existing systems of record. Suddenly, an AI agent can retrieve a document, analyse it, pass the output to another agent, and trigger a downstream action without human intervention, creating a level of automation that was previously out of reach.

This transition marks a profound change in the relationship between lawyers and their technology. AI agents are moving into the realm of autonomy and action – all while making decisions that can influence client matters, internal processes, and regulatory exposure.

A new type of risk

For lawyers, this is where the governance challenge becomes acute. A model that drafts a clause incorrectly is inconvenient, but a lawyer still reviews it before it reaches a client. By contrast, an autonomous agent that drafts an incorrect clause and then automatically updates a contract and emails it to the client isn’t just inconvenient – it’s a potential disaster.

The moment an AI system begins to influence the sequence or substance of legal work, the risk profile changes, introducing new points of failure that traditional oversight models were never designed to handle.

What if an AI agent relies on outdated documents or inconsistent records that distort its  reasoning? A single superseded clause in a precedent bank, for instance, can propagate errors across dozens of matters if an agent relies on it.

Moreover, there is an evidentiary dimension to consider. Courts increasingly expect parties to demonstrate the provenance of documents, data, and analysis. If an AI agent has touched a piece of work – whether by summarising a bundle of documents, extracting clauses, or drafting a chronology – firms will need to show how that output was generated and which sources it relied upon. An inability to demonstrate the chain of reasoning behind an agent’s conclusions could undermine both litigation strategy and regulatory compliance, not to mention landing a firm in hot water with their client.

What all this means is that firms must treat agentic behaviour not as a technical upgrade, but as a new category of operational risk.

Agentic workflows create a governance imperative

Most legal teams experimenting with agentic AI will likely begin with relatively “uncomplicated” workflows, like having an agent retrieve a precedent from a knowledge repository and send it to a fee earner. Another might post a draft answer to a Teams or Slack channel for review.

Another early use case might be internal knowledge support. Firms already field a constant stream of questions from lawyers and business services teams: HR policies, conflicts procedures, billing rules, client onboarding requirements. Traditionally, these queries land in shared inboxes or are escalated to support teams. AI agents offer a scalable alternative. A chatbot can draw from authoritative internal policies, generate a draft response, and route it to a human reviewer before it is released.

Once AI agents begin taking actions – even modest steps like the ones above – governance becomes a necessity. Firms must determine where AI is permitted to operate, where human oversight is mandatory, and where AI must be excluded entirely. This is not simply a matter of operational preference; it is a matter of risk management.

Some tasks – such as routing internal messages or retrieving documents – pose minimal risk. Others, such as approving client communications, interpreting contractual obligations, or making decisions that could influence litigation strategy, carry significant professional and regulatory implications. These tasks demand human judgment and may need to remain strictly off limits to autonomous systems.

The challenge is that workflows evolve. What begins as a simple automation can, over time, expand into areas where errors carry major consequences. Without clear guardrails, firms risk allowing AI to drift into zones where mistakes could breach confidentiality, misstate legal positions, or expose the firm to negligence claims. Governance, therefore, is not a one-time exercise – it needs to be an ongoing effort.

Data integrity supports good governance

For AI agents to act responsibly, they must operate on accurate, authoritative information. This is particularly critical when the distinction between a draft and a final version of a document can carry real consequences. A mislabeled contract, an outdated policy, or an unverified precedent can mislead an AI system and produce flawed output at scale.

This is why information architecture becomes central to safe AI adoption. Firms must identify which datasets are authoritative, consolidate them into a single source of truth, and ensure that content is curated and kept current – a discipline that becomes increasingly important as agents touch more of the workflow.

The bottom line? Information architecture matters. This means finding and maintaining critical datasets and creating a curated repository is essential. This information must be reviewed, validated, and pruned on a predictable schedule to ensure agents are working with the best, most relevant, and most up to date information. It’s the only way to ensure that AI supported workflows don’t introduce risks that lawyers never intended to take.

Lawyers and agents coexist

The next phase of AI adoption in law will not be defined by whether firms deploy agents, but by how effectively they supervise them. In effect, AI agents will require the same accountability structures that govern human lawyers and support staff. Those that ignore the governance component that this new AI demands will find themselves exposed to risk they did not anticipate, while those that embrace it will unlock new levels of efficiency and client service.

Either way, the age of agentic AI has arrived. The question for the legal profession isn’t whether agents will “become a part of everyday work”, but whether firms are prepared to safely manage the work they do every day.