Enhance README with execution and outcome signal insights#16
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Expanded the README to explain the importance of execution and outcome signals, their implications for trust in AI systems, and related industry directions.
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Pull request overview
This PR expands the Agent Signals documentation to clarify why execution signals are inherently lossy, why outcome signals provide durable verifiability, and how the gap between the two supports trust/calibration in agent systems.
Changes:
- Adds a new section contrasting execution vs. outcome signal capture reliability and the trust implications of each.
- Adds a “Related Industry Direction” section tying the protocol to broader verification/oversight trends.
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| **Design implication:** don't build your trust story on execution signals | ||
| alone. Anchor it to something durable. Treat execution signals as valuable- | ||
| but-lossy self-report, and treat outcome signals as the verifiable backbone. |
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| Frontier model providers are shipping architectures that separate capability | ||
| from verification. | ||
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| Anthropic's Claude Fable 5 and Mythos 5 (2026) expose the same base model | ||
| through two tiers: a generally-available tier (Fable 5) with external safety | ||
| classifiers that intercept high-risk requests and route them to a fallback | ||
| model, and a restricted tier (Mythos 5) without those classifiers, available | ||
| only to vetted partners. The two tiers differ not in capability but in the | ||
| safeguards and vetting applied around access. |
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| against independent evaluation, over time.* The calibration gap between what an | ||
| agent claims about its own work and what an independent evaluation confirms is | ||
| a number you can track run over run. |
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Expanded the README to explain the importance of execution and outcome signals, their implications for trust in AI systems, and related industry directions.