From Chatbots to Agents: The Real Promise of Agentic AI (and Why It Starts with Workflow Ownership)

Agentic AI is the newest innovation buzzword in legal technology. Vendors are racing to declare their tools agentic, promising transformation of every workflow from drafting to review. But in this frenetic moment, many products claiming to be agents are, in reality, little more than chatbots with a single integration.  This may be a contributing factor to why nearly half of AI projects will be scrapped by 2027, according to Gartner.  

Clearly there is untold business value if organizations can get beyond buzzwords and ChatGPT chatbots to the real promise of agentic AI – but how?

Defining What’s Agentic

Before we can evaluate any claims, we first need clarity: what actually makes an AI system agentic? A true agentic system consists of three interconnected components — a reasoning layer, tool access, and workflow ownership.

The reasoning layer, often powered by a large language model (LLM), interprets both a lawyer’s intent and the results of its own actions, continuously planning next steps based on context. The tool access layer connects to the systems where real work happens, such as document management repositories, databases, email, and drafting software. Finally, workflow ownership allows the agent to complete multi-step legal tasks end-to-end, without constant human input.

Imagine drafting a contract. A true agent can identify missing clauses, locate relevant precedents, redraft them to fit the context, define new terms, and insert the result back into the agreement — all while recording each step it took. That is a far cry from a static template tool or a one-click automation that simply fills names in an NDA.

Without all three elements — reasoning, integration, and workflow ownership — an agent is simply automation with a new label. And understanding that distinction is the first step toward separating genuine progress from marketing noise.

Why Workflow Ownership Matters

Once the definition is clear, the next question is: why does workflow ownership matter so much?

For lawyers, work rarely happens in isolation. Drafting, negotiating, and finalizing a document requires simultaneous interaction across multiple systems — the DMS, Microsoft Word, email, internal databases, and often several plug-ins.

An authentic agent must mirror that complexity. A single-integration product cannot reproduce the way legal professionals actually work. And if it can’t, it isn’t agentic. This distinction also touches cost and practicality. 

Agentic systems are computationally expensive — often involving multiple models and orchestration layers. They should therefore be reserved for problems that require that level of intelligence. As we often say, agentic AI is the most expensive way to solve a problem — so make sure it’s a problem worth solving.

When built and deployed correctly, however, these systems can replicate nuanced, multi-tool workflows at a fraction of the time, freeing professionals to focus on analysis, negotiation, and client strategy.

From here, the conversation naturally turns to why adoption has still been so slow — even as the technology becomes more capable.

Trust: The Cornerstone of Adoption

The greatest barrier to AI adoption in law isn’t capability — it’s trust. Lawyers remain responsible for every output an AI system produces, even when they didn’t generate it themselves. That creates natural hesitation to hand over control.

This may also explain why so many pilots stall before production — research consistently shows that a large share of AI initiatives never move beyond the pilot stage, often because users lack confidence in the systems’ reliability and transparency.

Trust grows through explainability. A well-designed agent doesn’t just return an answer; it shows its reasoning and its sources. Think of it as the digital equivalent of a math teacher saying, “Show your work.” When an agent exposes how it retrieved clauses, what documents it referenced, and what edits it made, the lawyer can review and validate each step — transforming AI from a black box into a visible, auditable collaborator.

Explainability also sets the stage for regulatory confidence. As firms integrate AI into client-facing work, detailed audit trails will become as essential as the output itself.

But trust isn’t just about the system. It also depends on what data feeds it — which leads to the next crucial consideration.

Your Data, Your Advantage

Even the most sophisticated agent is only as strong as the data it draws on. If the underlying repository is disorganized, outdated, or contaminated with client-specific information, the results will be equally flawed.

For law firms, this makes proprietary knowledge the new competitive moat. Every clause, contract, and playbook captures institutional expertise that no external model can replicate. Structuring and maintaining that data — through metadata, version control, and ongoing curation — ensures agents operate on the firm’s best, most accurate information.

In practice, this shifts the role of knowledge-management teams from custodians of static libraries to stewards of dynamic, AI-ready knowledge ecosystems. Clean data doesn’t just support accuracy; it safeguards a firm’s differentiation and pricing power in an increasingly commoditized market.

Once firms recognize that connection — between trusted systems and trusted data — the next logical step is to understand how to evaluate the technology itself.

Questions Every Buyer Should Ask

As adoption accelerates, separating meaningful innovation from marketing requires sharper evaluation. When assessing “agentic” solutions, firms should insist on specifics:

1. Workflow depth – What end-to-end legal tasks can the system complete without human input?
2. Tool access – Which integrations (DMS, Word, Outlook, etc.) does it rely on, and how securely?
3. Data handling – Where does the data live, and how is accuracy and provenance tracked?
4. Transparency – Can the system show its reasoning and create a verifiable audit trail?
5. Cost efficiency – Why does this problem justify an agentic approach versus a simpler model?
6. Sustainability – What is the environmental and compute cost of each deployment?

These questions aren’t just due diligence — they’re essential filters to distinguish true agentic capability from mere branding. The right vendor will welcome them. The wrong one will hide behind terminology.

The Human in the Loop

At this point, one thing becomes clear: agentic AI is not about replacing lawyers — it’s about scaling their reach. When agents automate routine retrievals, comparisons, and insertions, they give legal professionals more time to exercise judgment, creativity, and strategic thought. Transparency keeps humans firmly in the loop — validating outputs, teaching the model through feedback, and continuously refining workflows.

Self-correction further strengthens that partnership. A mature agentic architecture includes internal checks — sometimes deterministic tools or validation models — that review outputs for consistency before presenting them to the user. In other words, the agent learns to check its own homework.

This human-machine collaboration forms the bridge between trust and transformation — the point where adoption becomes sustainable.

Balancing Power and Responsibility

As adoption widens, firms and corporate legal departments will face new choices around governance and sustainability. Running complex models at scale consumes significant computing resources — and, increasingly, environmental ones. That reality reinforces a simple principle: use the right tool for the right job.

Not every task warrants an agentic engine; sometimes traditional automation remains the most responsible choice. True innovation lies not in how many AI agents a firm deploys, but in how intelligently they are applied — solving the right problems with clarity and control.