Building Smarter Systems for Legal Workflows

The promise of AI in legal work is clear: faster analysis, better drafting, and support for increasingly complex tasks. But to achieve this, we need systems that go beyond one-off answers or static chatbots.

At Definely, we’re building an agentic system - a structured, multi-step framework that mirrors the way lawyers actually work: through reasoning, referencing, drafting, and iterating. It’s an approach that doesn’t just deliver AI-powered tools, but rethinks how those tools collaborate to complete meaningful tasks.

This isn’t about recreating a lawyer. It’s about engineering systems that can support the unique, high-stakes workflows legal teams navigate every day.

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From Static Chat to Structured Assistance

Traditional AI interfaces operate like sophisticated search bars: you ask a question, get a response and move on. But legal work rarely happens in a single step. Reviewing a contract might mean extracting obligations, interpreting cross-referenced clauses, comparing language to policy, and generating compliant revisions - all within a single task.

That kind of work requires AI systems that can work across multiple steps, draw from various sources, and retain context over time. These are the hallmarks of what’s known as agentic systems: AI frameworks that plan, act and adapt across a defined task flow.

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Fordification: A Useful Model for AI-Driven Legal Work

To explain what agentic systems look like in practice, it helps to draw from something familiar: the assembly line. In the early 20th century, Ford revolutionised manufacturing by breaking complex production into smaller, specialised tasks, executed in sequence by dedicated stations. Each step added value, and together they produced faster, more consistent results.

We see something similar happening in digital work, and legal workflows are a perfect fit for this model.

At Definely, we call this the Fordification of legal AI. Each AI agent in our system has a specific role: for example, one retrieves the relevant clause, another summarises it, another checks for  inconsistencies, and another generates a proposed revision. Together, these agents form a chain that can tackle complex legal tasks more reliably than a monolithic model or a single chat prompt ever could.

This structure gives us control, transparency, and, most importantly, the ability to align each agent’s behaviour with the standards and expectations of legal work.

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How We’re Building It

We’re using a framework called LangGraph to orchestrate these agents and define how they interact. LangGraph allows us to model workflows as stateful, dynamic systems, meaning agents don’t just pass information to one another, they remember where they’ve been, loop back to clarify if needed, and adapt based on evolving context.

This structure also allows us to break down legal tasks into composable, auditable steps. Each action - whether it’s fetching a definition, analysing a clause chain or generating a redraft - is transparent and traceable.

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Why It Matters

Legal work is high-stakes. Lawyers need systems they can rely on  - not just to save time, but to uphold standards of quality, accuracy and transparency.

An agentic system lets us build those safeguards in. Instead of relying on one massive output from a language model, we create modular actions that are easier to control, review, and refine. Lawyers remain at the centre of the workflow, but the system around them becomes far more capable.

It also enables richer user experiences. A lawyer using Definely Enhance can now have a dialogue with the assistant: summarising a clause, then asking follow-up questions, then pulling in related content from Vault, then generating a revision. All of this happens inside Microsoft Word, with no loss of context and no switching between tools.

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Looking Ahead

We believe this structured, agent-driven approach is the future of AI in legal tech. It allows us to solve more meaningful problems, while maintaining the trust, control and transparency legal teams demand.

As we continue to evolve Enhance, we’ll expand these capabilities further: multi-agent coordination, deeper document context, memory across sessions, and, eventually, task-level workflows that support entire review and drafting cycles.

This is the foundation we’re building toward: AI that doesn’t just answer questions, but helps you get legal work done.

Interested in how this is evolving? Schedule a session with one of our LegalTech experts to find out more.

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