Aderant Launches Agent Center for Law Firm Back-Office AI Automation

The legal business management software company Aderant used its Momentum Global 2026 conference to announce the Aderant Agent Center, a framework for deploying AI agents across law firm financial and operational workflows. The announcement, made May 12, 2026, marks a shift from isolated automation toward coordinated intelligent systems.

According to the official press release, the Agent Center builds on the company's Stridyn platform and is powered by its MADDI AI layer. Three agents launch initially: a Collections Agent that automates and prioritizes collections workflows based on payment risk, an Appeals Agent that identifies e-billing rejections and drafts data-informed responses, and a Talent Evaluation Agent that aggregates matter-level feedback into structured insights.

Chris Cartrett, President and CEO of Aderant, framed the focus around processes that are both tedious and financially consequential. "When you're billing a client and something gets challenged, then you have to submit an appeal — it's a very manual process, but it's really important because it's about money," Cartrett said during a press briefing ahead of the conference. The physical reality of this work involves clicking through billing portals, typing appeal narratives, and waiting days for responses — now the agents handle the initial legwork.

Lisa Erickson, senior vice president of product management and AI, characterized the initial agents as "operational workflow agents," a category she distinguished from simpler automation or system-level agents. The approach ties agents tightly to firm data, with humans remaining in the loop on approvals while the agents learn and improve over time (a necessary guardrail given the financial stakes).

Aderant currently has 12 AI products in the market and plans to deliver another 14 by year-end. The Agent Center roadmap calls for between 13 and 15 total agents, with the three unveiled at Momentum representing the initial release. More than 275 firms have adopted Expert Sierra, the company's cloud-enabled practice management platform, with 23% in the Am Law 200.

On development velocity, Cartrett pointed explicitly to AI coding tools. "Literally 95% of the code that they were able to create and 100% of the automated testing over the top of that was able to be enhanced literally via AI," he said, citing use of Claude Code, GitHub Copilot, and Cursor. He also credited Stridyn's architecture for enabling faster builds by reducing reliance on legacy code.

Independent reporting from Legal Technology corroborates the agent count and conference details, noting a further 10 agents are planned for release post-Momentum. The outlet also highlighted the onsite hackathon where Aderant's development team will build a prototype feature over approximately 30 hours, chosen by attendee vote from five client-sourced ideas.

Other innovations highlighted include AI-powered time capture and narrative generation, the Virtual Pricing Director for data-driven pricing and budgeting, and next-generation accounts receivable and general ledger tools. Aderant operates as a business unit of Roper Technologies (Nasdaq: ROP).

The Agent Center represents a logical extension of Aderant's existing AI investments, but the real test lies in adoption. Law firms have historically been cautious about automating financial workflows where errors carry direct monetary consequences. Whether the agents can earn enough trust to move beyond human-in-the-loop approvals remains the actual question.

For now, the hackathon prototype unveiled at the closing reception serves as a public demonstration of development capabilities. Cartrett described it as putting money where the mouth is. Whether that translates into measurable efficiency gains for the 275+ Expert Sierra firms is something only time and billing cycles will reveal.

Artūras Malašauskas is an AI Systems Integrator with 20+ years of production-grade web engineering experience. He has designed, shipped, and scaled enterprise Python/PHP systems for logistics, SaaS, and public-sector clients. For the past year, he has focused exclusively on AI integrations: deploying open-source LLMs, building generative media pipelines (image, audio, video), and engineering multi-agent workflows for real production environments. His standard: reproducibility, security, cost-efficient inference—no vaporware. He documents and evaluates emerging AI tooling, separating verified capabilities from marketing noise. Technical editor at: muza-ai.eu, ai-verslas.lt, ai-naujinos.lt Connect on LinkedIn