For the past two decades, legal workflow software has done one thing well. It takes a process a partner has mapped out, encodes it as templates and approval gates, and runs it the same way every time. The market is mature. Litera Transact (formerly Closing Folders) coordinates closings. HighQ runs collaboration sites and matter workflows. iManage Work is the document and email backbone in most large firms. NetDocuments plays the same role for many mid-sized firms. ContractPodAi and Ironclad manage contract lifecycles end-to-end.
These tools work. They are not what AI agents replace.
What AI agents change is the category of work the workflow tools have never been good at. Reviewing a 400-page disclosure bundle to find inconsistencies. Summarising thirty years of board minutes for a regulatory submission. Reading every clause in a portfolio of leases and producing a position paper. The work that does not fit into a template and is currently done by associates and paralegals working long evenings.
This article maps where each technology fits, what AI agents do that traditional workflow software cannot, and what an architecture that uses both looks like in practice.
What traditional legal workflow software does well
Traditional legal workflow software is built around a model of work that is well understood. A matter has a defined lifecycle. The lifecycle has stages. Each stage has documents, tasks, deadlines, and approvers. The software’s job is to make that lifecycle visible, enforceable, and auditable.
Matter management platforms like HighQ and Litera’s Foundation Dragon handle this through configurable workflow templates. When a corporate transaction begins, the firm picks a template (acquisition, divestment, financing) and the system creates the matter site, document folders, task list, and approval routing. When a deadline approaches, the system notifies the responsible lawyer. When a document is uploaded, the system applies access controls and audit logging.
Document management systems like iManage and NetDocuments handle the document layer. They enforce versioning, permissions, and retention. They integrate with Outlook so emails attached to a matter are filed without manual intervention. They provide the search index that allows a partner to find every document she has ever touched on a specific client.
Transaction management tools like Litera Transact handle closings. The system tracks every signature page, every condition precedent, every closing deliverable. It coordinates the dozens of parties involved in a typical mid-market transaction and produces a closing bible that takes minutes to compile rather than days.
Contract lifecycle management systems like Ironclad and ContractPodAi handle the post-execution life of contracts. They run approval workflows, store metadata, track renewal dates, and link contracts to obligations.
All of this is rules-based, deterministic, and reliable. A firm can predict exactly what the system will do in any situation, because the system only does what it was configured to do. That predictability is the reason these tools have become the operational backbone of modern legal practice.
It is also the reason they cannot handle the work that AI agents are now demonstrating they can.
Where rule-based workflow software falls short
Rule-based workflow software requires the work to be structured before the software touches it. Someone has to design the template. Someone has to define the routing rules. Someone has to specify what counts as a complete document and what counts as an exception. The software then runs the work according to that specification.
This model breaks down in three places.
Unstructured documents. A workflow system can store a contract, but it cannot read one. If a transaction lawyer needs to know which of fifty target-company contracts contain a change of control provision, the workflow system can locate the contracts and apply review tasks. It cannot read them. The reading is done by a person, billed by the hour, often well into the evening before a sign-off deadline.
Judgement-heavy review. A workflow system can route a draft for partner review, but it cannot perform the review. The work of reading thirty pages, comparing them against precedent, identifying departures from the firm’s standard position, and explaining the implications belongs to a human. The workflow system tracks that the review happened. It does not contribute to the review itself.
Exception handling. Templates assume the matter follows the standard path. When it does not, when a counterparty introduces a novel structure, when a regulator changes its position mid-transaction, when a due diligence finding requires the deal to be restructured, the workflow system has no view. It continues to route tasks according to the original template until someone manually intervenes to reconfigure it.
The result is that the work which is most expensive (judgement) and most variable (exceptions) sits outside the workflow system. The system manages the process around that work but does not reduce its volume.
This is the gap that AI agents address.
What AI agents do differently
An AI agent is not a workflow engine. It is a system that can read unstructured material, reason about it, retrieve relevant context, produce a written output, and either complete a task or hand it to a human with the evidence required to verify the answer. The difference from a workflow tool is not incremental. It is categorical.
A workflow system routes tasks. An agent performs tasks.
In practice, this means a few things. An agent can take a sale and purchase agreement and a list of disclosure documents, read all of them, and produce a structured summary of every disclosure that materially affects a warranty in the SPA. It can cross-reference the firm’s precedent database, flag clauses that depart from the standard position, and produce a report that the supervising partner reviews rather than drafts.
An agent can monitor a regulator’s publication feed, identify changes that affect a specific client’s authorisations, and draft an advisory note that summarises the change, identifies the affected obligations, and proposes next steps. The note arrives in the partner’s inbox with citations and reasoning, ready for review.
An agent can read every lease in a real estate portfolio, extract the key commercial terms, identify clauses that create unusual landlord obligations, and produce a position paper that a transaction lawyer uses to brief the client.
None of this work fits inside a workflow template. None of it is rule-based. All of it has historically been done by humans because no other technology could read the documents and reason about them.
The capability that makes this possible is not new model architecture. It is the combination of three things: large language models that can read and reason at human level on legal text, retrieval-augmented generation that grounds the model’s output in the firm’s own knowledge base rather than its training data, and the Model Context Protocol that lets agents connect to a firm’s existing systems (iManage, HighQ, NetDocuments, SharePoint) and act on what they find there.
Where each technology belongs
The right way to think about AI agents and traditional legal workflow software is not as competitors. They address different layers of legal work.
| Layer | Tool category | What it does |
|---|---|---|
| Document storage and access | iManage, NetDocuments | Manages versions, permissions, retention, search |
| Matter coordination | HighQ, Litera Foundation | Tracks tasks, deadlines, approvals, collaboration |
| Transaction execution | Litera Transact | Coordinates signature collection and closing |
| Contract lifecycle | Ironclad, ContractPodAi | Manages approvals, renewals, obligations |
| Document review and analysis | AI agents | Reads, summarises, compares, drafts |
| Exception handling and judgement | AI agents | Reasons about non-standard situations |
A firm that adopts AI agents is not removing the workflow tools. It is filling a gap above them, in the layer where unstructured legal work happens.
The integration is structural. Agents need access to the documents in the document management system. They need to be triggered by events in the workflow system. They need to write their outputs back to the matter file. The Model Context Protocol provides the connectors for this without requiring the firm to migrate any of its existing systems.
In a typical deployment, an agent might be triggered when a workflow task in HighQ requests document review. The agent retrieves the documents from iManage, runs the review against the firm’s precedent database, produces a structured summary, writes the summary back to the matter file in HighQ, and routes the task to the supervising partner with a complete audit trail of what the agent did and why. The workflow system tracks the task; the agent does the work.
What changes for the firm
For partners and senior associates, the change is in where their time goes. The work that previously consumed evenings (reading every page of a disclosure bundle, summarising every board minute, comparing every clause to precedent) is done by the agent. The work that remains is the work that requires legal judgement: deciding what to do about the issues the agent has surfaced.
This is not a marginal productivity improvement. The Thomson Reuters 2025 Future of Professionals report, which surveyed 2,275 professionals across more than 50 countries, found that respondents expect AI to save five hours per week per professional, equivalent to roughly 240 hours and around $19,000 of value per person annually. Eighty percent of respondents expect AI to have a transformative or high impact on their work within five years. The firms moving fastest are not using AI to write faster. They are using it to remove categories of work entirely from the human queue.
For operations leaders and chief technology officers, the change is in how technology gets deployed. AI agents are not packaged products that sit alongside existing systems. They are workflows that read from and write to existing systems, governed by the same access controls, audit requirements, and supervision standards that apply to humans. The integration model is closer to a new associate joining the team than to a new software product being procured.
For risk and compliance teams, the change is in what governance looks like. Traditional workflow software provides audit logs of who did what. AI agents need an additional layer: audit logs of what the agent considered, what it retrieved, what reasoning it followed, and what confidence it had in its output. This is the foundation of supervision under the SRA’s Risk Outlook on AI in the legal market, which makes clear that solicitors remain accountable for AI outputs and that this accountability requires the ability to inspect and verify the system’s reasoning.
The architecture that supports both
Adding AI agents to an existing legal technology stack works when three architectural conditions are met.
Agents run inside the firm’s infrastructure. Documents in iManage, NetDocuments and HighQ contain privileged client material. An agent that reads those documents and reasons about them must run on infrastructure the firm controls. This typically means deployment in the firm’s own Azure or AWS tenancy, in a UK region, with the firm holding its own encryption keys. Sending privileged material to a third-party SaaS AI service creates a cross-border data transfer that the firm’s professional obligations and the post-Schrems II data protection regime do not permit without substantial additional safeguards.
Agents integrate through existing protocols. Agents that read from iManage, write to HighQ, and post to Microsoft Teams need standardised connectors. The Model Context Protocol provides this. It allows agents to connect to the firm’s existing systems through configured tool servers rather than through bespoke integrations that need to be rebuilt for every new system.
Agents are supervised through the same model as humans. Every agent action is logged. Every output carries a confidence score that determines whether it can proceed automatically or requires human review. Every interaction is reviewable by a partner. The audit trail is immutable. This satisfies the SRA’s accountability requirements and provides the evidence base that risk and compliance teams need.
A firm that already runs iManage, HighQ, and Litera Transact does not need to replace any of those tools to adopt AI agents. It needs to add a layer that connects them, runs on the firm’s own infrastructure, and operates within the same governance framework that already applies to the rest of the practice.
When traditional workflow tools are still the right answer
There are workflows where AI agents add little. Transaction closings are a process problem, not a reading problem. The work of collecting signature pages, tracking conditions precedent, and producing a closing bible is exactly what Litera Transact is designed for. Adding an agent to it would not improve the outcome. Matter intake, conflict checking, and engagement letter generation are similarly well-served by existing tools, because the work is rule-based and the documents are structured.
The simplest test: if the work can be specified as a flowchart and runs the same way every time, a traditional workflow tool is the right answer. If the work requires reading unstructured material, reasoning about it, and producing a non-template output, an AI agent is the right answer.
Most firms have both kinds of work, and most will need both kinds of tools.
What this means for procurement
For firms evaluating AI tools alongside existing workflow software, the procurement question is not “should we replace HighQ with an AI tool” or “should we add Harvey to our stack”. It is “where in our existing matter lifecycle is unstructured judgement work consuming the most associate and partner time, and what would change if an agent handled it”.
The answer typically points to a small number of high-volume document review and analysis tasks: due diligence in M&A, lease review in real estate, regulatory monitoring across financial services clients, contract review for procurement teams. These are the workflows where the gap between what the workflow software can do and what the work actually requires is largest, and where agents produce the most visible change.
The integration with existing workflow tools determines whether the deployment succeeds. An AI tool that requires the firm to abandon its existing matter management system or its existing document repository will not be adopted. A platform that connects to those systems, reads from them, writes to them, and works within the firm’s existing supervision and audit framework can be deployed in weeks without disrupting the rest of the practice.
That is the question worth asking of any AI vendor: not whether the agent is impressive in isolation, but whether it works inside the firm’s existing technology stack and the firm’s existing professional obligations. The firms moving fastest are the ones that have answered both.