Tokenless

Financial services ai

How to Build Voice Agents That Don't Fall Apart When You Interrupt - Jesse Hall, LiveKit

How to Build Voice Agents That Don't Fall Apart When You Interrupt - Jesse Hall, LiveKit

Jesse Hall of LiveKit explains how to build voice agents for financial services that feel human rather than robotic. The talk covers why model leaderboards mislead for real-time conversations, why time to first audio and time to first sentence matter more than first byte or first token, and how async tool calls keep a conversation flowing during slow back-end work. It also covers audio-aware turn detection, the roughly 1.5-second latency budget, and benchmarking your own stack with simulated callers.

An AI Agent Got Its User Banned From Resy — Keycard, JPMorgan Chase & AAuth

An AI Agent Got Its User Banned From Resy — Keycard, JPMorgan Chase & AAuth

A panel of experts discusses the challenges of granting autonomy to AI agents due to coarse-grained authorization systems. They advocate for intent-based runtime authorization, leveraging concepts like "missions" and "budgets" from AOTH, brain-hands architecture, and dynamic policy management. The discussion covers agentic commerce, accountability through tamper-resistant audit logs, and whether P(doom) is a real threat, emphasizing engineering ingenuity and societal learning from failures.

AI Agents Cut 3-Day Portfolio Builds to Minutes — Jack McDonald, Apollo & Michael Manapat, Rowspace

AI Agents Cut 3-Day Portfolio Builds to Minutes — Jack McDonald, Apollo & Michael Manapat, Rowspace

Jack McDonald of Apollo's Redding Ridge and Michael Manapat of Rowspace discuss where AI becomes mission-critical in structured credit and CLO management. They cover compressing portfolio construction from days to minutes, automating the weekly trade reconciliation, and why embedding engineers with front-office teams, rather than relying on generic LLMs, is what earns trust in financial data.

The Production AI Playbook: Deploying Agents at Enterprise Scale — Sandipan Bhaumik, Databricks

The Production AI Playbook: Deploying Agents at Enterprise Scale — Sandipan Bhaumik, Databricks

Sandipan Bhaumik presents a five-pillar framework for successfully moving AI systems from demos to production, inspired by a retail bank's failed chatbot PoC. The framework covers defining numerical success (Evaluation), tracing every AI decision (Observability), building robust data pipelines (Data Foundation), managing multiple AI interactions (Multi-agent Orchestration), and ensuring accountability and security (Governance). He illustrates these concepts with a banking chatbot case study, emphasizing continuous evaluation, data quality, and a proactive incident playbook.