The transition from AI as a chatbot to AI as an autonomous agent requires more than just better models; it requires agents capable of regulating their own state and context at scale.

In this episode of The Hedgineer Podcast, co-hosts Michael Watson and Jhanvi Virani sit down with Mitch Troyanovsky, co-founder of Basis, an agent platform specifically designed for the accounting industry. The conversation moves beyond the hype of generative AI to address the engineering realities of building "agent-native" enterprises. Mitch explains why the next frontier of applied machine learning involves closing the loop on self-improving agents—systems that can optimize their own trajectories, contexts, and tools without constant human intervention.

We explore the "single pane of glass" debate: whether specialized platforms like Basis will remain the system of record or if frontier model interfaces will eventually consolidate all enterprise workflows. The discussion delves into the technical nuances of Recursive Language Models (RLMs) and the "Better Intelligence" approach, where models are leveraged to programmatically curate their own context windows to maintain performance over long-duration tasks.

The episode also tackles the cultural shift required for AI adoption. From implementing "Do You Stand By This" (DYSB) protocols to ensure accountability, to the "lexical taxonomy" required to write documentation specifically for LLM consumption rather than human readers, we provide a blueprint for firms looking to move from experimental AI to production-grade agentic systems.

Key Takeaways:

Closing the Applied ML Loop: Why the next generation of agents will focus on self-regulation and autonomous state management to handle production workloads.
The "Database-ification" of SaaS: How AI agents interacting via API threaten the value proposition of traditional software UIs, potentially reducing many SaaS tools to mere structured data stores.
Recursive Language Models (RLMs): A technical look at using model intelligence to dynamically curate context at every forward pass, moving beyond simple "append-only" context windows.
Writing for Machines: Why traditional human writing styles are inefficient for LLMs and how "information density" is becoming a critical engineering discipline.

About the Guest:

Mitchell Troyanovsky is the co-founder of Basis, a New York-based platform building AI agents for the accounting industry. He is a leading voice on the future of agentic systems at scale and the implementation of Recursive Language Models in production.

About Hedgineer

Hedgineer is building the AI platform for institutional investing — deploying agents, skills, and data connectors directly inside hedge funds and asset managers to transform investment and operational workflows.

The Hedgineer Podcast follows CEO Michael Watson and COO Jhanvi Virani as they navigate the frontier of AI adoption in finance, sharing unfiltered perspectives from the teams, guests, and problems they work with every day.

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Watch the full episode on Spotify or YouTube at youtube.com/@hedgineer.

Connect with us on LinkedIn at linkedin.com/company/hedgineer-io or reach out at podcast@hedgineer.io.

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Beyond the Chatbot: Building Agent-Native Enterprises with Mitchell Troyanovsky | S3E4

The Hedgineer Podcast

Beyond the Chatbot: Building Agent-Native Enterprises with Mitchell Troyanovsky | S3E4