Artificial intelligence has moved well beyond chatbots that answer questions. A new generation of AI systems – called AI agent – can now plan, decide, and act on their own, without a human approving every step. For the legal profession, this shift is not just a technological curiosity: it raises urgent questions about liability, oversight, and the future of legal work itself. This post explains what AI agents actually are, how they differ from the AI tools most lawyers already know, and why understanding them is quickly becoming a professional necessity.
From Chatbot to Co-Worker: What Makes an AI Agent Different
Most people’s experience with AI is still largely conversational. You type a prompt, the system responds, and you decide what to do with that response. This is what tools like ChatGPT or Copilot do in their basic form – they assist, but they do not act independently.
An AI agent is fundamentally different. Rather than waiting for a new instruction after every output, an AI agent is given a high-level goal and then determines on its own how to achieve it. It plans a sequence of steps, selects and uses external tools (such as databases, APIs, or email systems), evaluates the results of each action, and adapts its approach accordingly – all without a human guiding each individual move.
To use a concrete example: instead of asking an AI „summarize this contract,“ an AI agent might be told „review all supplier contracts signed in the last 12 months, flag those with problematic termination clauses, draft a summary report, and send it to the legal team.“ The agent would then execute that entire workflow autonomously, making decisions along the way.
This capacity for multi-step, autonomous execution is what distinguishes agents from earlier AI tools and what makes them both powerful and legally complex.
How AI Agents Actually Work
At their core, AI agents are built on large language models (LLMs), which serve as the agent’s reasoning engine. But the LLM alone is not enough. Agents also rely on memory modules (to retain context across a task), planning modules (to break a goal into actionable steps), and tool access (to interact with external systems and data sources).
The agent operates in a continuous perception-decision-action loop: it reads its environment, decides what to do next, takes an action, observes the result, and adjusts its plan. This cycle repeats until the goal is reached – or until a governance boundary requires a human to step in.
It is also important to distinguish between a single AI agent and a multi-agent system. A single agent handles one defined task end to end. Multi-agent systems coordinate several specialized agents that work in parallel, hand off tasks to one another, and together execute complex, organization-wide workflows. Legal platforms like LexisNexis‘ Protégé already deploy this model, with a dedicated orchestrator agent directing a research agent, a web search agent, and a document agent simultaneously.
AI Agents Are Already Entering the Legal Sector
This is not a future scenario. AI agents are entering law firms and legal departments right now. Thomson Reuters launched agentic workflows through its CoCounsel platform in early 2026, offering autonomous document review and deep research capabilities. Industry analysts estimate that by 2026, 40% of enterprise applications will include task-specific AI agents – up from less than 5% just a few years ago.
Corporate legal departments are moving particularly fast. According to survey data, AI adoption among in-house legal teams more than doubled in a single year, jumping from 23% to 52%. Notably, 64% of those teams now expect to rely less on external counsel as a direct result of the capabilities they are building with AI agents internally.
This is a structural shift – not just for how legal work is done, but for who does it, and who is responsible when something goes wrong.
The Legal Questions AI Agents Raise
For lawyers, AI agents do not just present a new tool to evaluate. They generate a set of genuinely difficult legal questions that the profession is only beginning to work through.
Who is liable when an AI agent makes a mistake?
If an AI agent autonomously reviews a contract, misses a critical clause, and that error causes a client financial harm, the question of accountability becomes complex. The agent itself cannot be held legally responsible. But the answer does not automatically fall on the developer either. Depending on how the system was configured, deployed, and supervised, liability may rest with the law firm that integrated the tool, the lawyer who relied on its output without adequate review, or the vendor – or it may be shared across all three. Existing frameworks are not designed to cleanly resolve this.
What does human oversight actually mean for agentic systems?
The EU AI Act requires meaningful human oversight of high-risk AI systems. But with agents that operate across dozens of steps in minutes, „oversight“ cannot mean approving each individual action in real time. Legal professionals and their clients need to think carefully about what oversight checkpoints are built into agentic workflows, and whether those checkpoints are genuinely adequate or merely formal.
Does GDPR still apply and how?
AI agents often continuously collect and process personal data across shifting contexts as they complete a task. The GDPR’s requirements around purpose limitation, data minimisation, and transparency were not designed with this kind of dynamic, multi-step data processing in mind. A single agentic deployment can simultaneously trigger data protection obligations, consumer protection law, cybersecurity requirements, and intellectual property concerns – all at once.
Can AI agents enter into legally binding commitments?
Some agentic systems are already capable of making bookings, placing orders, and initiating payments on behalf of users. Whether such actions constitute legally binding commitments and under what conditions, remains an open question in most jurisdictions.
The EU AI Act and Agentic AI: A Regulatory Gap
The EU AI Act (Regulation 2024/1689) applies to AI agents through its risk-based framework, but legal experts are increasingly flagging that the regulation was not specifically designed with agentic systems in mind.
One particular challenge is what researchers call „behavioral drift“ – the risk that an agent’s actions evolve over time in ways that were not anticipated when it was first deployed. Analysts have concluded that high-risk agentic systems with untraceable behavioral drift cannot currently satisfy the AI Act’s essential requirements. The harmonized technical standards that would clarify exactly how compliance must be demonstrated have been delayed to late 2026, leaving providers in an uncertain position.
Regulators are paying close attention. In March 2026, a consortium of UK regulators including the CMA, FCA, ICO, and Ofcom published a foresight paper identifying a five-level autonomy spectrum for agents, warning that the greater the autonomy, the more acute the governance challenge. Their conclusion – shared broadly across EU and UK regulators – was unambiguous: agentic AI does not fall outside existing regulatory frameworks. The obligations of transparency, fairness, and accountability continue to apply. Organizational responsibility is not transferred to the machine simply because the machine acts autonomously.
What Lawyers Should Do Now
Awareness is the starting point. Lawyers who do not understand what AI agents are, and what they can and cannot do, are poorly positioned to advise clients on their use – or to protect themselves when deploying these tools in their own practice.
More concretely, legal professionals should begin asking hard questions about the AI tools they or their organizations use. Are those tools merely generative, or do they operate agentically? What actions can they take autonomously? What audit trails do they produce? Who bears responsibility if the output is wrong?
For those advising corporate clients, the EU AI Act’s August 2026 compliance deadlines for high-risk AI systems make this conversation time-sensitive. Organizations using AI agents in consequential decisions – in hiring, contract review, compliance monitoring – need legal guidance now, not after the first enforcement action.
Final Thoughts
AI agents represent a genuine leap beyond what most legal professionals have encountered so far. They are not smarter chatbots – they are autonomous systems capable of executing complex workflows, making decisions, and taking real-world actions with legal consequences. The law is still catching up. Regulatory frameworks like the EU AI Act are working to address agentic AI, but significant gaps remain. For lawyers, that gap is not only a compliance challenge – it is also a professional opportunity. Those who understand agentic AI first will be best equipped to advise on it, regulate it, and shape the frameworks that govern it. The time to build that understanding is now.
Stay curious, stay informed, and let´s keep exploring the fascinating world of AI together.
This post was written with the help of different AI tools.
