# gaps

topic: Agent Behavior Verification
category: AI safety/alignment
severity: critical
analyzed_at: 2026-03-03T19:58:26.216743+00:00
description:
  While many drafts address agent identity and authentication, few tackle how to verify that an agent is actually behaving according to its declared capabilities and policies. There's a critical gap in runtime behavioral attestation and compliance monitoring mechanisms.

evidence:
  High overlap in identity/auth (108 drafts) but only 44 drafts on safety/alignment, with no specific focus on behavioral verification


topic: Cross-Domain Agent Liability
category: Policy/governance
severity: critical
analyzed_at: 2026-03-03T19:58:26.216743+00:00
description:
  When autonomous agents operate across organizational boundaries and cause harm or make decisions with legal implications, there's no standardized framework for liability attribution. The policy/governance drafts don't address cross-jurisdictional legal accountability.

evidence:
  91 policy/governance drafts but legal liability for cross-domain autonomous actions remains unaddressed


topic: Agent Resource Exhaustion Protection
category: Autonomous netops
severity: high
analyzed_at: 2026-03-03T19:58:26.216743+00:00
description:
  Missing standardized mechanisms to prevent malicious or poorly designed agents from consuming excessive network, compute, or storage resources. Current drafts focus on traffic management but not on agent-specific resource quotas and enforcement.

evidence:
  93 autonomous netops drafts and 73 ML traffic management drafts lack agent-specific resource protection mechanisms


topic: Human Override Protocols
category: Human-agent interaction
severity: critical
analyzed_at: 2026-03-03T19:58:26.216743+00:00
description:
  Critical gap in standardized protocols for humans to safely interrupt, override, or take control of autonomous agents in emergency situations. Only 30 drafts address human-agent interaction, with no focus on emergency takeover procedures.

evidence:
  Only 30 human-agent interaction drafts compared to 213+ autonomous operation drafts, with no emergency override standards


topic: Agent-Generated Data Provenance
category: Data formats/interop
severity: high
analyzed_at: 2026-03-03T19:58:26.216743+00:00
description:
  While 145 drafts address data formats for AI interop, there's insufficient attention to tracking the provenance and lineage of data generated by agents. This creates trust and auditability issues in agent-to-agent data exchanges.

evidence:
  145 data format drafts with high overlap but no clear standards for agent-generated data provenance tracking


topic: Agent Capability Degradation Handling
category: AI safety/alignment
severity: high
analyzed_at: 2026-03-03T19:58:26.216743+00:00
description:
  No standardized approaches for detecting and handling when an agent's capabilities degrade due to model drift, data corruption, or hardware issues. Systems need graceful degradation protocols rather than silent failures.

evidence:
  Only 44 safety/alignment drafts don't address capability degradation, while 213+ drafts assume stable agent performance


topic: Multi-Agent Coordination Deadlocks
category: A2A protocols
evidence: 120 A2A protocol drafts with high internal overlap but no systematic deadlock prevention frameworks
severity: high
analyzed_at: 2026-03-03T19:58:26.216743+00:00
description:
  With 120+ A2A protocol drafts, there's insufficient attention to preventing deadlock situations where multiple agents create circular dependencies or resource conflicts. Missing are standardized deadlock detection and resolution mechanisms.


topic: Agent Privacy Preservation
category: Agent identity/auth
severity: high
analyzed_at: 2026-03-03T19:58:26.216743+00:00
description:
  Agents often process sensitive data but current drafts don't adequately address privacy-preserving computation, differential privacy, or secure multi-party computation for agent interactions. This is critical for deployment in regulated industries.

evidence:
  108 identity/auth drafts focus on authentication but lack privacy preservation mechanisms for agent data processing


topic: Real-time Agent Debugging
category: Other AI/agent
severity: medium
analyzed_at: 2026-03-03T19:58:26.216743+00:00
description:
  Missing standardized protocols for debugging autonomous agents in production environments. When agents make unexpected decisions, there are no standard interfaces for real-time introspection without disrupting operations.

evidence:
  26 other AI/agent drafts suggest various approaches but no standardized debugging protocols for production agents


topic: Agent Firmware/Model Update Security
category: Model serving/inference
evidence: 42 model serving drafts but no comprehensive security standards for agent software/model updates
severity: high
analyzed_at: 2026-03-03T19:58:26.216743+00:00
description:
  While model serving is addressed in 42 drafts, there's insufficient focus on secure update mechanisms for agent models and firmware. Missing are standards for cryptographically verified, rollback-capable agent updates.


topic: Cross-Protocol Agent Migration
category: A2A protocols
severity: medium
analyzed_at: 2026-03-03T19:58:26.216743+00:00
description:
  No standardized mechanisms for migrating agent state and context when moving between different A2A protocols or infrastructure providers. This creates vendor lock-in and limits agent mobility.

evidence:
  120 A2A protocol drafts with high overlap suggest competing approaches but no migration standards between them


topic: Agent Energy Consumption Optimization
category: ML traffic mgmt
severity: medium
analyzed_at: 2026-03-03T19:58:26.216743+00:00
description:
  Missing standards for energy-aware agent deployment and operation. As AI workloads are energy-intensive, there's no framework for agents to optimize their energy consumption or for infrastructure to enforce energy budgets.

evidence:
  73 ML traffic management drafts focus on performance but lack energy consumption considerations for sustainable AI deployment


