Three Eras of AI Coding Agents: Tool Calls, Long Tasks, and Orchestration

Theo Browne uses three model releases to explain how he sees coding agents developing.

TBTheo Browne연사 · Ping Labs
채널
Theo - t3․gg
게시일
2026년 7월 10일
영상
0:33
읽기
1분
슬라이드
1
원문
영어
YouTube에서 열기
스크롤하면 영상이 오른쪽 아래로 작아져 계속 재생됩니다. 자막은 읽기 언어를 따릅니다.
읽기 언어2개 언어로 읽을 수 있음
이 글은 아직 한국어 번역이 없어 영어 원문으로 표시합니다.
요약

30초 요약

Theo Browne describes three eras of agentic coding through Sonnet 3.5, Opus 4.5 and Mythos 5. In his view, the progression runs from reliable tool calls to longer tasks and then to orchestrating and verifying delegated work.

  1. 1Tool callsSonnet 3.5 was not the first model with tool calls, but Theo found them reliable enough for daily coding work.
  2. 2Long-running workTheo says Opus 4.5 could work for longer without losing track of its task.
  3. 3OrchestrationHe describes Mythos 5 as a jump toward delegating parts of a task to other models and verifying their work.

핵심 정리

  1. Theo associates Sonnet 3.5 with reliable tool calls for everyday coding.
  2. He sees Opus 4.5 as a step toward longer-running tasks.
  3. He associates Mythos 5 with breaking work up, delegating it and verifying the results.
전문

챕터별로 다듬은 전문 읽기

원본 자막만 보기
01

Three eras of coding agents

Theo connects three model releases with tool calling, long-running work and orchestration. This is his framework for understanding their progress.

I think of these as eras. Sonnet 3.5 represents the tool-call era: it was not the first model able to call tools, but, in my experience, it was the first to do so consistently and reliably enough for everyday coding.

Three stages of coding agents
슬라이드 1 / 1Three stages of coding agentsTheo’s slide pairs Sonnet 3.5 with tool calls, Opus 4.5 with long-running work, and Mythos 5 with orchestration.

Then came Opus 4.5, which could work on much longer-running tasks without losing track of what it was doing.

Mythos 5 feels like another jump, this time to orchestration. In my experience it understands not only the codebase but how to use other models: it can split up work, spawn additional agents, and have the results verified afterward when asked.

같은 채널의 다른 글

슬라이드 5장38:47EN
Theo - t3․gg· 2026년 10월 28일Theo’s Long-Running Agent Experiments: Verification, Compute and CostTheo Browne · Ping LabsA skateboard game and two compiler projects show how repeated checks and flexible compute can sustain agent work—and how expensive those loops can become.코딩 에이전트에이전트 엔지니어링AI cost & performance+2
슬라이드 5장36:07EN
Theo - t3․gg· 2026년 10월 27일Theo on Token Costs, Coding Agents, and the Price of AI CapabilityTheo Browne · Ping LabsA viral estimate of Lauren's AI spending prompts a closer look at cache pricing, benchmarks, and the changing work of coding agents.AI cost & performance코딩 에이전트Inference+1
슬라이드 5장30:37EN
Theo - t3․gg· 2026년 10월 26일Theo’s Three Skills for Reviewing and Shipping with Coding AgentsTheo Browne · Ping LabsHow sol-loop, full-send and full-stop encode review, verification and different levels of merge authority.Agent skills코딩 에이전트Code review & verification+2
슬라이드 2장0:43EN
Theo - t3․gg· 2026년 10월 23일Theo Distinguishes Security Vulnerabilities from Active AttacksTheo Browne · Ping LabsA short explanation of why disclosed software flaws and reported attacks are different kinds of security news.CybersecuritySoftware engineering
슬라이드 6장29:22EN
Theo - t3․gg· 2026년 10월 22일Theo on Security Vulnerabilities, Stolen API Keys and AITheo Browne · Ping LabsA look at the gap between vulnerability disclosure and patching, and a speculative account of why stolen inference keys may be valuable.CybersecurityAI securitySoftware engineering+1
슬라이드 4장31:13EN
Theo - t3․gg· 2026년 10월 20일Theo on Why Smarter Models Help More Than DevelopersTheo Browne · Ping LabsFrom game modifications to compiler experiments, Theo revisits his assumptions about who benefits from more capable AI.코딩 에이전트Developer productivityHuman-AI Collaboration+1