CoCounsel has been around long enough that many lawyers initially filed it mentally under “another legal chatbot.” The more interesting shift, based on how people describe using it in day-to-day practice, is that it’s moving from passive Q&A into more agentic workflows—meaning it can take a goal (“summarize these depo transcripts,” “draft a motion outline,” “find the issues in this contract”) and execute a multi-step process across documents with less hand-holding.
In online lawyer communities, that change is usually framed less as “AI magic” and more as “it finally fits into how work actually happens”: messy inputs, time pressure, and the need to verify everything.
What CoCounsel’s Agentic AI Can Do Now
CoCounsel’s agentic features are best understood as “task runners” rather than a single prompt-and-response. Users describe giving it a bundle of materials—contracts, correspondence, deposition transcripts, discovery responses, medical records—and asking for structured outputs: chronologies, issue lists, comparisons, or draft sections.
The key change is the expectation that it can plan and complete multiple sub-steps (extract, organize, synthesize) in one workflow, instead of requiring you to prompt it through each stage. In practice, lawyers say that feels less like chatting and more like delegating a first-pass assignment to a junior: it produces something you can edit, cite-check, and use as a starting point.
Another commonly discussed “what changed” is reliability around document-grounded answers—people want the tool to stay tethered to what’s in the record. In forum threads, lawyers repeatedly emphasize that the only outputs they trust are those that clearly trace back to the provided materials (or that can be cross-checked quickly). The agentic angle shows up when CoCounsel is asked not merely to summarize, but to pinpoint where it found each fact, identify inconsistencies across sources, or surface “what’s missing” (e.g., gaps in a timeline or references to exhibits that weren’t produced). That’s the difference between “nice summary” and “usable litigation support,” and it’s where many commenters say AI becomes worth opening during a busy week.
Finally, lawyers talk about agentic features as “repeatable workflows.” Instead of crafting a new prompt each time, the same kind of task can be run across matters: summarize every depo the same way, generate the same contract risk table each deal, or produce the same medical chronology format each personal injury case. This is where the time savings compound—less mental overhead, fewer bespoke prompts, and fewer opportunities to miss steps. The practical benefit isn’t that it replaces legal judgment; it’s that it standardizes the grunt work so you can spend your energy on strategy, arguments, and client communication.
Real-World Use Cases Shared by Lawyers Online
One of the most frequently mentioned use cases is deposition and testimony work. Lawyers online describe uploading a transcript (or multiple transcripts) and asking for: a tight fact summary, a list of admissions, impeachment material, inconsistent statements, and “topics I should follow up on.”
The agentic part is that the tool can scan long text, pull out key Q/A sections, and reorganize them into litigation-friendly outputs—often including a chronology of events, a witness-by-witness comparison, or a theme list tied to elements of claims and defenses. Users still stress that you must verify quotes and page/line references, but many say it meaningfully reduces the time to get from “I have 300 pages” to “I know what matters.”
Another practical pattern lawyers share is drafting and revising: motion outlines, demand letters, discovery responses, and internal memos. In discussions, the most successful approach is treating CoCounsel as a first-draft engine after you supply constraints: jurisdiction, posture, the client’s goals, key facts, and—critically—excerpts from the record you want it to rely on. Lawyers also describe using it as a “second set of eyes” to spot missing elements: for example, asking it to check whether a draft motion actually addresses each factor in the applicable test, or whether a contract clause revision introduced an unintended ambiguity. The win here is speed and completeness, not automatic correctness—online users emphasize that anything going out the door still needs attorney review.
A third cluster of use cases involves high-volume document work: contract review and diligence, discovery review triage, and medical record summarization. People report good results asking for structured tables—key dates, obligations, notice requirements, termination triggers, indemnities, limitation of liability, governing law, and so on—especially when they need to compare multiple agreements quickly.
In litigation contexts, lawyers talk about using it to build timelines from scattered exhibits, identify references to attachments that are missing from production, or generate issue-spotting lists for follow-up interrogatories and depositions. The practical, “real lawyer” takeaway from these online conversations is consistent: AI is most valuable when it outputs something structured (tables, chronologies, checklists) that you can audit fast, rather than paragraphs of prose that sound right but are harder to validate.
CoCounsel’s shift toward agentic AI features is less about novelty and more about workflow: multi-step task completion, document-grounded synthesis, and repeatable formats that match how legal work is actually produced. The real-world use cases lawyers describe—depo analysis, drafting support, and high-volume summarization—show the same theme: it’s best used as a force multiplier for first-pass work, with verification and judgment staying squarely on the attorney.
If you adopt it with that mindset—structured tasks in, auditable outputs out—you’re much more likely to get consistent value without over-trusting the machine.