My first heavy duty job with GPT 5.6 sol blew me away, kudos! I suggest you send this sentence to your Codex and wait silently for the output results. You will come back and thank me: "Comprehensive review of my past codex usage and operations, list some very bad usage methods and give me suggestions for improvement." I asked GPT-5.6 Sol to do a complete runtime audit of the Codex usage in the past 3 months. The audit scope includes: About 1,125 sessions 6.5GB original trace 61 types of long-term tasks Code, research, content production, Feishu, public account, X, automation and multi-agent workflow The audit entry is placed directly on config, AGENTS, hooks, session, automation status and delivery evidence. The most valuable capability of GPT‑5.6 Sol this time is to reduce scattered configuration issues, automated idling, and delivery misjudgments into a set of runtime, governance, and observability issues. After the strong model enters the real project, it can serve as a runtime reviewer: track state drift along the trace, check the permission boundary, and complete the evidence chain for each "done". The stronger the model capability, the more important the harness, checkpoint, readback and recovery design are.
Visual summary
Article argument map
Generated from the post's content graph
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-technical-distribution["technical distribution"]
topic-retrieval["retrieval"]
capability-agent-workflow["agent workflow"]
capability-harness-engineering["harness engineering"]
capability-long-running-agents["long-running agents"]
capability-ai-native-workbench["AI-native workbench"]
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-technical-distribution
format-long_post -->|covers| topic-retrieval
format-long_post -->|signals| capability-agent-workflow
format-long_post -->|signals| capability-harness-engineering
format-long_post -->|signals| capability-long-running-agentsVisual structure
Essay structure map
Built from summary and key paragraph positions
Mermaid outline
flowchart LR
thesis["My first heavy duty job with GPT 5.6 sol blew me away, kudos! I suggest you send this sentence to your Code..."]
signal["My first heavy duty job with GPT 5.6 sol blew me away, kudos! I suggest you send this sentence to your Code..."]
operator["My first heavy duty job with GPT 5.6 sol blew me away, kudos! I suggest you send this sentence to your Code..."]
implication["My first heavy duty job with GPT 5.6 sol blew me away, kudos! I suggest you send this sentence to your Code..."]
thesis -->|frames| signal
signal -->|develops| operator
operator -->|lands in| implication