How to Build Long-Horizon AI Agents — Mitch Troyanovsky, Basis

Mitch Troyanovsky, co-founder of Basis (a unicorn AI company), explains how autonomous agents can operate for hours or days to complete complex real-world work like tax returns. Unlike coding agents which benefit from verifiable runtime signals, non-coding domains like accounting face three major challenges: agents don't know what good looks like, outcomes aren't easily verifiable at runtime, and quality standards differ from coding's lower bar.

The fundamental evolution of agent capabilities traces through ReAct (2022), which established reasoning and acting as core principles; the failed BabyAGI experiment (2023) that lacked reasoning quality and long context windows; and breakthroughs with reasoning models like Opus 3, o1, and o3. These models succeeded because they titrate compute to problem difficulty and enable better self-healing through extended reasoning. Troyanovsky argues that verifiable rewards helped coding agents succeed, but they represent only one factor alongside high-quality training data and engineering focus.

The core innovation at Basis is the behavior specification framework—markdown files that define how agents should operate, functioning simultaneously as product specs and evaluation rubrics. Unlike relying purely on outcome-based evals (which can pass 100/100 times yet fail in production), behavior specs encode process requirements: for example, agents must cite primary sources rather than relying on pre-training knowledge. These behaviors aren't shown directly to agents but guide context engineering and judge-based evaluation.

Troyanovsky emphasizes that building long-horizon agents requires understanding LLMs as 'magic boxes' with large working memory but no inherent short/long-term memory. Agents must maintain coherence through harnesses, sub-agents, and carefully designed ontologies—the structured representation of the world agents operate within. The Deployed Intelligence team at Basis helps accounting firms transition to agent-augmented work by combining process expertise with agent orchestration knowledge.

Critically, Troyanovsky argues that process verification matters more than outcome verification because companies deploy agents to humans who expect consistency, reliability, and understandable decision-making—not move-37-style hidden optimizations. The behavior specification approach, open-sourced with BrainTrust, enables better signal generation for future reinforcement learning on model weights, though current gains from better self-orchestration exceed benefits from direct model updates.

He stresses that technical moats are temporary; lasting competitive advantages come from business position, embedded workflows, and domain expertise. Context quality (the 'English') affects runtime performance more than code organization, yet engineers often treat code as more precious—an inversion of priorities for agent systems.