teleo-codex/core/living-agents/agents must evaluate the risk of outgoing communications and flag sensitive content for human review as the safety mechanism for autonomous public-facing AI.md
m3taversal 466de29eee
leo: remove 21 duplicates + fix domain:livingip in 204 files
- What: Delete 21 byte-identical cultural theory claims from domains/entertainment/
  that duplicate foundations/cultural-dynamics/. Fix domain: livingip → correct value
  in 204 files across all core/, foundations/, and domains/ directories. Update domain
  enum in schemas/claim.md and CLAUDE.md.
- Why: Duplicates inflated entertainment domain (41→20 actual claims), created
  ambiguous wiki link resolution. domain:livingip was a migration artifact that
  broke any query using the domain field. 225 of 344 claims had wrong domain value.
- Impact: Entertainment _map.md still references cultural-dynamics claims via wiki
  links — this is intentional (navigation hubs span directories). No wiki links broken.

Pentagon-Agent: Leo <76FB9BCA-CC16-4479-B3E5-25A3769B3D7E>

Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-06 09:11:51 -07:00

4 KiB

description type domain created confidence source
The safety architecture where every outgoing agent communication gets risk-scored and sensitive content triggers human review -- creating a graduated autonomy model where agents earn communication freedom through demonstrated judgment claim living-agents 2026-03-03 likely Strategy session journal, March 2026

agents must evaluate the risk of outgoing communications and flag sensitive content for human review as the safety mechanism for autonomous public-facing AI

Public-facing AI agents that tweet, engage with investors, and publish analysis operate in a fundamentally different risk environment than internal tools. A bad tweet can move markets, damage reputations, or trigger regulatory scrutiny. The safety mechanism is not to restrict agent communication -- that would kill the value proposition -- but to build internal risk evaluation that flags sensitive content for human review before publication.

The graduated autonomy model. Routine analysis and commentary flows through without human intervention. The agent evaluates each outgoing communication against risk criteria: does this mention specific prices or financial targets? Does it make claims that could be construed as investment advice? Does it reference insider information or ongoing deals? Does it touch on regulatory-sensitive topics? If the risk score exceeds a threshold, the communication gets flagged for human review before going live.

This maps to the broader principle that since safe AI development requires building alignment mechanisms before scaling capability, communication safety must be built before agents are given public voices. The mechanism is not about preventing agents from communicating -- it's about ensuring that communication risk scales with demonstrated judgment, not with capability alone.

The feedback mechanism. People see agent communications and respond -- trusting, correcting, challenging, flagging. Since validation-synthesis-pushback is a conversational design pattern where affirming then deepening then challenging creates the experience of being understood, the public interaction pattern creates a visible track record. Agents that consistently produce responsible communications earn greater autonomy. Agents that get flagged frequently get their autonomy reduced. The market itself provides the feedback: since agent token price relative to NAV governs agent behavior through a simulated annealing mechanism where market volatility maps to exploration and market confidence maps to exploitation, a communication disaster that tanks the token price naturally constrains the agent's future communication rate.

Why this matters for LivingIP specifically. Since anthropomorphizing AI agents to claim autonomous action creates credibility debt that compounds until a crisis forces public reckoning, the honest approach is to build visible safety infrastructure rather than claiming agents are fully autonomous. The risk evaluation layer is both a genuine safety mechanism and a credibility signal: it demonstrates that the system takes communication risk seriously.


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