Working Notes on Agent Systems/Brad Zhang

@teach_fireworks / X longform

AI engineers are shifting from writing code to managing Agents. After Agent throughput...

AI engineers are shifting from writing code to managing Agents. After Agent throughput continues to improve, the most difficult resource to expand becomes Human Attention. This...

July 14, 2026 · 1 min read

AI engineers are shifting from writing code to managing Agents. After Agent throughput...
Figure 1 / source image

AI engineers are shifting from writing code to managing Agents.

After Agent throughput continues to improve, the most difficult resource to expand becomes Human Attention.

This essence discusses the changes in the role of AI engineers: instead of polling ten terminals and personally responsible for the scheduler, router, and memory, the execution is handed over to the long-running Manager, which delegates workers, maintains the Persistent Context, and promotes tasks to continue running through Triggers.

The focus of engineering efforts gradually shifts to goal definition, loop design, verification links, and final decisions.

Model capabilities are just the base.

Whether work can move from Issues to Tests, Reviews, PRs, and Merges depends on Harness and organizational processes.

After Token and Compute are expanded, Human Attention will form a Review Queue.

Persistent Context, Delegation and Trigger form an extensible Agent Loop.

The Manager/Worker model keeps people at the direction and approval nodes.

Production value comes from the complete delivery chain, not the speed of a single code generation.

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FORMATTOPICCAPABILITYMARKETcoverscoverscoverscoverssignalssignalssignalssignalsFORMATlongform noteTOPICagentsTOPICharness engineeringTOPIClong running agentsTOPICmemoryCAPABILITYagent workflowCAPABILITYharness engineeringCAPABILITYlong-running agentsCAPABILITYproduct surface
Mermaid outline
flowchart LR
  format-long_post["longform note"]
  topic-agents["agents"]
  topic-harness-engineering["harness engineering"]
  topic-long-running-agents["long running agents"]
  topic-memory["memory"]
  capability-agent-workflow["agent workflow"]
  capability-harness-engineering["harness engineering"]
  capability-long-running-agents["long-running agents"]
  capability-product-surface["product surface"]
  format-long_post -->|covers| topic-agents
  format-long_post -->|covers| topic-harness-engineering
  format-long_post -->|covers| topic-long-running-agents
  format-long_post -->|covers| topic-memory
  format-long_post -->|signals| capability-agent-workflow
  format-long_post -->|signals| capability-harness-engineering
  format-long_post -->|signals| capability-long-running-agents
  format-long_post -->|signals| capability-product-surface

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Built from summary and key paragraph positions

AI engineers are shifting from writing code to managing Agents. After Agent throughpu...THESISAI engineers areshifting from writingcode to managingAgents. After AgentSIGNALAI engineers areshifting from writingcode to managingAgents.OPERATORWhether work can movefrom Issues to Tests,Reviews, PRs, andMerges depends onIMPLICATIONProduction value comesfrom the completedelivery chain, notthe speed of a single
Mermaid outline
flowchart LR
  thesis["AI engineers are shifting from writing code to managing Agents. After Agent throughput continues to improve..."]
  signal["AI engineers are shifting from writing code to managing Agents."]
  operator["Whether work can move from Issues to Tests, Reviews, PRs, and Merges depends on Harness and organizational..."]
  implication["Production value comes from the complete delivery chain, not the speed of a single code generation."]
  thesis -->|frames| signal
  signal -->|develops| operator
  operator -->|lands in| implication

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