Agentic AI is emerging as a transformative technology for collections and recovery, moving beyond traditional automation to systems that set goals, plan multi‑step actions, and adapt in real time. Panelists Chris Busse (SingleStone) and Dan Medina (Harris & Harris) explained that while automation follows a pre‑defined path, agentic AI “draws the path itself case by case.”
Examples included dispute handling, where agentic AI can assess whether accounts involve attorneys, bankruptcies, or healthcare claims, and then decide whether to act, escalate, or pause for human review. Chris Busse emphasized that agentic AI is “goal‑seeking or end‑to‑end task driven,” capable of calling additional tools to achieve outcomes. Dan Medina added that orchestration layers are critical to prevent runaway costs and ensure compliance, noting, “You usually want to tack on an else…abort, exit, or throw it in a queue for a human in the loop.”
The discussion also highlighted the importance of knowledge bases. Organizing institutional knowledge into accessible formats allows agents to learn from past decisions and build proprietary rule sets. Open‑source frameworks like OpenKB and LangChain were cited as cost‑effective tools for agencies to experiment with, while governance and supervisory controls remain essential to safe deployment.
🧠 Key Takeaways:
- Start Small with Pilots: Identify high‑value use cases such as dispute resolution or compliance workflows, and test agentic AI with human oversight.
- Build Knowledge Bases: Convert documents and institutional decisions into structured repositories to prepare for AI integration.
- Establish Safeguards: Implement orchestration layers, exit strategies, and supervisory controls to manage costs and compliance risks.
This webinar underscored that agentic AI is not just automation—it’s adaptive, goal‑driven, and capable of reshaping how collections teams operate. Agencies that begin experimenting now will be better positioned to leverage its full potential.
