AI agents for legal work: a guide to agentic AI

An AI agent is a software system that can carry out a multi-step task toward a goal with a degree of autonomy. Rather than answering a single question and stopping, it takes in a request, breaks the work into steps, draws on tools and data and then acts.  

In an in-house legal setting, that means software that can do more than respond to a prompt: it can review a contract against your standards and playbooks, extract the information inside it and carry out the next step, with a person staying in control of the outcome.

According to Thomson Reuters’ 2026 research, 77% of professionals expect agentic AI to become central to their workflows by 2030. But greater autonomy doesn’t mean handing legal decisions over to AI. The more useful question is where agents can take on the process, while lawyers retain judgment and control.

This guide explains what AI agents and agentic AI actually are, how they differ from the chatbots and assistants most people have already used, and what a controlled agentic approach looks like in practice for legal teams.

What are AI agents and agentic AI?

An AI agent is a system designed to work toward a goal across multiple steps. Given an instruction, it can plan a sequence of actions, use information or tools to carry them out, check its own progress and produce a result. The defining feature is that it works through a task rather than returning a single response.

Agentic AI is the broader approach of building software and systems around agents. Instead of one model answering one question, an agentic system could coordinate multiple specialized steps, each handling part of a larger job. In practice, this is what allows AI to take on workflows, such as reviewing a contract end to end, rather than isolated queries.

How are AI agents different from chatbots and assistants?

The easiest way to understand agents is to look at how much of the task the technology is taking responsibility for.

For example, you could ask a chatbot: “What does this indemnity clause mean?”. It will answer the question and the exchange ends there.

An agent could take a specific contract, find the indemnity clause, compare it against the company’s playbook, identify where it falls outside the approved position and suggest alternative wording. The chatbot gives you information, but the agent helps move the work forward.  

An agentic system can coordinate several tasks as part of a defined workflow that doesn’t give AI unrestricted control. A good agentic system operates within the data, instructions, tools and approval points that an organization has established.

Where can AI agents be used in legal work?

Agentic AI is most useful when it’s applied to the parts of legal work that are repetitive, high in volume and process driven. Common use cases include:

• Contract review. Agents can analyze a contract, compare its terms against your standards or playbooks, identify deviations and risks and prepare redlines and redrafts for a lawyer to approve.  
• Contract data extraction. Agents can read agreements and pull out structured information, such as parties, dates, values and obligations, turning unstructured documents into data you can search and report on.
• Portfolio-level questions. Instead of reviewing contracts one at a time, an agent can work across a repository to answer questions like "which agreements contain automatic renewal clauses?", "which contracts have a particular liability position?" or "where are we consistently accepting terms outside our preferred position?"‍
• Intake and triage. Agents can receive incoming requests or third party documents, classify them and route them to the right place with the right context attached.
• Drafting from templates. Agents can generate first drafts from approved templates, clauses and business rules.
• Tracking obligations and dates. Agents can monitor signed agreements for key dates and obligations so renewals and deadlines aren’t missed.

In each of these cases, the agent handles the legwork while the lawyer keeps responsibility for judgment, negotiation and the final decision.

What are the risks of agentic AI and does it still need human oversight?

Agentic AI introduces real capability, but also some real risks that legal teams need to take seriously, such as:

• Accuracy – AI can be confidently wrong. Outputs need to be checked, and the higher the stakes, the more important that verification is.
• Confidentiality and data governance – legal data is sensitive, and it matters where your information goes, who can access it and whether it’s used to train external models. These questions need to be answered before any tool touches live contracts. Always check your AI vendor’s security and data policy when deciding whether to use a tool.
• Transparency and defensibility – if an agent reaches a conclusion, you should be able to see how. Outputs that can’t be explained are hard to rely on and harder to defend.
• Accountability – legal is an advisory function, and when something goes wrong, responsibility sits with people, not software. This approach also reflects current professional guidance. The American Bar Association’s Formal Opinion 512 says lawyers using generative AI must apply their existing professional duties, including an appropriate level of independent review of AI outputs.
• Over-reliance – the goal is to support lawyers, not to replace their judgment. Agents work best inside clear guardrails, such as approved playbooks and human approval steps, rather than operating unsupervised. For example, your contract review agent will only be as good as the playbook it checks against.

How does Summize use AI agents for legal work?

Summize takes a multi-agent approach through SIA – our Summize Intelligence Agents.

Rather than relying on an AI agent to handle every part of a complex task, SIA uses specialized agents for different stages of the process. Our Chief Technology Officer Richard Somerfield describes the approach as similar to how legal professionals work in teams: one agent can generate content, one can validate it and another can redline.  

{{quote 1}}

The approach is designed around the reality that legal teams need more than speed – they need outputs they can understand, challenge and ultimately take responsibility for.

{{testimonial 1}}

In Summize, SIA is applied across contract workflows through capabilities including AI-powered contract review and redlining in Microsoft Word, and Ask SIA, which allows users to ask natural language questions across contract and workflow information.

Security is central to this approach. Summize is built to protect sensitive contract data, with safeguards designed to prevent customer information from being shared across customers or used to train external AI models.

Agentic AI needs access to the right contract knowledge, playbooks and workflows to produce useful results. At Summize, that means bringing AI agents into the contracting process with the context they need to produce relevant outputs and the checks that help legal teams stay in control. Find out how we’re putting this approach into practice.

[Button: Explore our agentic approach >]