# gaps

topic: Multi-Agent Consensus Protocols
category: A2A protocols
severity: high
analyzed_at: 2026-03-04T00:06:32.798946+00:00
description:
  While A2A protocols exist, there's no standardized approach for multiple AI agents to reach consensus on shared decisions or conflicting objectives. This becomes critical when agents must coordinate on resource allocation or policy enforcement across domains.

evidence:
  High overlap in A2A protocols suggests fragmented approaches without consensus mechanisms, critical for autonomous system coordination


topic: Agent Behavioral Verification
category: AI safety/alignment
severity: critical
analyzed_at: 2026-03-04T00:06:32.798946+00:00
description:
  No drafts address how to verify that deployed AI agents are actually behaving according to their declared policies or specifications. This creates a gap between policy definition and runtime assurance.

evidence:
  Only 47 drafts on AI safety despite 433 total drafts, and behavioral verification is fundamental to autonomous system trust


topic: Cross-Protocol Agent Migration
category: Agent discovery/reg
severity: medium
analyzed_at: 2026-03-04T00:06:32.798946+00:00
description:
  Agents need to migrate between different network protocols, domains, or infrastructure providers while maintaining state and identity. Current drafts focus on registration but not migration continuity.

evidence:
  Agent registration servers exist but no migration protocols, essential for dynamic edge deployments mentioned in technical ideas


topic: Real-Time Agent Rollback Mechanisms
category: Autonomous netops
evidence: 114 autonomous netops drafts but no rollback mechanisms, critical for production network safety
severity: high
analyzed_at: 2026-03-04T00:06:32.798946+00:00
description:
  When autonomous agents make incorrect decisions, there's no standardized protocol for quickly rolling back their actions across distributed systems. This is critical for network operations and safety.


topic: Agent Resource Accounting and Billing
category: new
severity: medium
analyzed_at: 2026-03-04T00:06:32.798946+00:00
description:
  Multi-tenant environments need standardized ways to track and bill for AI agent resource consumption across compute, network, and storage. Current drafts define resources but not accounting mechanisms.

evidence:
  Edge AI placement and resource types defined but no economic models for multi-tenant agent deployments


topic: Federated Agent Learning Privacy
category: Policy/governance
severity: high
analyzed_at: 2026-03-04T00:06:32.798946+00:00
description:
  While federated architectures exist, there's insufficient specification for privacy-preserving agent learning that prevents data leakage between federated participants during model updates.

evidence:
  Federated AI architectures mentioned but private data training requirements suggest unresolved privacy challenges


topic: Agent Capability Negotiation
category: A2A protocols
severity: medium
analyzed_at: 2026-03-04T00:06:32.798946+00:00
description:
  Agents need standardized protocols to negotiate their capabilities and limitations before collaboration. Tool enumeration exists but not dynamic capability negotiation during runtime.

evidence:
  Tool enumeration APIs exist but no dynamic capability negotiation, needed for heterogeneous agent coordination


topic: Cross-Domain Agent Audit Trails
category: Agent identity/auth
severity: high
analyzed_at: 2026-03-04T00:06:32.798946+00:00
description:
  When agents operate across multiple domains or organizations, there's no standardized format for maintaining audit trails that satisfy different regulatory requirements simultaneously.

evidence:
  Cross-domain identity management exists but audit requirements vary by jurisdiction, creating compliance gaps


topic: Agent Failure Cascade Prevention
category: AI safety/alignment
severity: critical
analyzed_at: 2026-03-04T00:06:32.798946+00:00
description:
  No protocols exist to prevent agent failures from cascading across interconnected autonomous systems. This is critical as agent interdependencies increase in production deployments.

evidence:
  Limited AI safety focus (47 drafts) despite high interconnectivity implied by A2A protocols and autonomous netops


topic: Human Override Standardization
category: Human-agent interaction
severity: high
analyzed_at: 2026-03-04T00:06:32.798946+00:00
description:
  While human-agent interaction exists, there's no standardized protocol for human operators to override autonomous agent decisions across different vendor implementations and domains.

evidence:
  Only 33 human-agent interaction drafts suggest limited focus on human control mechanisms essential for safe deployment


topic: Agent Performance Benchmarking
category: new
severity: medium
analyzed_at: 2026-03-04T00:06:32.798946+00:00
description:
  No standardized metrics or protocols exist for comparing agent performance across different implementations, making it difficult to evaluate and select appropriate agents for specific tasks.

evidence:
  AI workload characterization mentioned but no performance comparison frameworks, needed for agent selection and optimization


