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Mitch Troy (co‑founder of TryBasis) argues long‑horizon agents must be built…

2026-08-06 · 16:29 UTC ·@mattturck ·1 min read

Brief

Matt Turck’s conversation with Mitch Troy (TryBasis) contends that truly long‑horizon AI agents need behavior specifications, ontologies, and process supervision. Troy says recent reasoning‑model breakthroughs (Opus 3, o1, o3) made agents viable because LLMs lack long‑term memory; verification must focus on process adherence (accountant‑style checks, documentation‑as‑code, RL, and context as runtime training data) to scale multi‑day autonomous work.

Why it matters

Mitch Troy (co‑founder of TryBasis) argues long‑horizon agents must be built around explicit behavior specs, ontologies, and process supervision to handle multi‑day tasks (example: autonomous, multi‑day tax returns and agents that hand off like senior engineers) (08:24, 10:19, 36:37).

Key details

  • Troy claims LLMs lack long‑term memory and that AutoGPT failed; three reasoning‑model breakthroughs — labeled Opus 3, o1, o3 — unlocked practical agents by enabling process/step verification rather than relying on single‑shot outputs (12:33, 14:13, 15:51, 17:07, 18:23).
  • Verification should mirror accountants: check non‑deterministic work via documented processes and judges; treat 'context' as runtime training data; use documentation‑as‑code, open‑source behavior specs, and RL for behavior adherence to scale and onboard many agents (26:55, 42:18, 01:04:20, 54:56, 01:14:29).
Source evidence

How to build long-horizon AI agents: behavior specs, ontologies, process supervision - my conversation with @mitch_troy, co-founder of @trybasis

01:09 Why Everyone at Basis Was Whispering to AI when @steph_palazzolo walked in
04:12 Accounting as "an Intelligence Over the Economy"
06:11 What Makes an Agent Truly Long-Horizon
08:24 Inside an Autonomous, Multi-Day Tax Return
10:19 Agents That Hand Off Like Senior Engineers
11:17 A Brief History of Agents: From ReAct to Today
12:33 Why LLMs Have No Long-Term Memory
14:13 Why AutoGPT Didn't Live Up to Its Promise
15:51 The Three Breakthroughs: Opus 3, o1, o3
17:07 Why Reasoning Models Unlocked Agents
18:23 "Let's Verify Step by Step": The Road Not Taken
20:32 Pushing Back on the METR Chart
22:09 Why Coding Agents Won First
25:14 Why Real-World Agents Are Harder
26:55 How Accountants Verify Non-Deterministic Work
29:18 You Can't Scale Tax Returns Like Math
33:16 100 Evals Pass - So What?
35:53 Right Answer, Wrong Process
36:37 Behavior Specs, Explained
39:58 How Specific Should Behaviors Be?
42:18 Context Is Runtime Training Data
44:21 Who Judges the Judge?
46:45 The Move 37 Objection
50:02 The Magic Box Mental Model
52:41 "Nothing Has Changed Since o3"
54:56 Open-Sourcing Behavior Specs with @ankrgyl @braintrust
59:45 Ontologies: A World for Agents to Live In
01:04:20 Documentation as Codebase
01:06:33 Why the Founding Fathers Were Context Engineers
01:09:05 Onboarding 300 Brilliant Alien Employees
01:11:10 Self-Improving Agent Systems
01:12:50 The Context Mistake Agent Builders Make
01:14:29 RL on Behavior Adherence
01:17:01 Will the Bitter Lesson Swallow the Harness
01:18:46 "Technical Moats Are Not Real Moats"
01:21:03 Advice for AI Builders

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