Abstract
Autonomous multi-agent AI systems increasingly perform complex tasks through agent-to-agent communication, tool calls, external information sources, delegated workflows, and heterogeneous communication protocols. A fundamental reliability problem arises when an originating instruction is preserved at the message-integrity level but its governing conditions are altered, misinterpreted, or incorrectly applied during downstream agent processing. Conventional tracing, cryptographic hashes, message authentication, and output evaluations may not establish which observable processing boundary introduced a material instruction discrepancy or whether the change resulted from legitimate factual information or unauthorized instruction-like content.
More details at : https://zenodo.org/records/24076231
This technical disclosure presents a computer-implemented diagnostic architecture for condition-specific causal testing and evidence-bounded localization of instruction interpretation changes in distributed autonomous-agent systems.
The architecture constructs versioned, typed originating task contracts; establishes source-role eligibility for factual information and instruction modification; captures message-transformation lineage; and generates matched counterfactual interventions independently varying legitimate factual fields and unauthorized instruction-like assertions.
An adaptive witness-selection procedure selects diagnostic tests according to expected hypothesis discrimination, execution cost, and mandatory condition coverage. Protocol-independent diagnostic adapters execute the selected interventions in isolated, non-effectuating environments across agent communication interfaces, tool-calling systems, and multi-stage workflows.
A behavioral comparison processor evaluates deterministic or stochastic differences under documented test conditions, while an evidence-bounded localization processor identifies the earliest supported observable divergence boundary. Where evidence is incomplete, the architecture reports bounded processing intervals or unresolved hypotheses without asserting unsupported historical causation.
Additional embodiments address branch-and-merge processing, source-instruction influence, Time-of-Check-to-Time-of-Use (TOCTOU) revalidation, selective diagnostic correction, regression durability, and independently verifiable evidence packages.
Big Tech Technical Relevance and Product Integration: The proposed architecture may complement Google Gemini Enterprise Agent Platform, Agent Development Kit (ADK), and Agent2Agent (A2A) by diagnosing instruction changes across remote-agent communication and orchestration boundaries; Anthropic Claude, Claude Code, and Model Context Protocol (MCP) by distinguishing legitimate external information from unauthorized tool-result instructions; OpenAI Agents SDK, Codex, and ChatGPT Work by analyzing agent handoffs, tool-call transformations, and instruction-to-action discrepancies; Microsoft Copilot Studio, Microsoft Foundry, and Agent 365 by extending enterprise agent observability with condition-specific diagnostic testing and fault localization; and Amazon Bedrock AgentCore by supplementing agent tracing and evaluation with controlled counterfactual witnesses and reproducible diagnostic evidence.
These integrations could help engineering teams identify where an instruction was altered or misapplied, distinguish source-induced behavioral changes from legitimate factual responses, and validate corrective changes without disrupting authorized agent operations.
The architecture is applicable to heterogeneous multi-agent systems, A2A, MCP, AI-agent orchestration, autonomous software agents, enterprise AI reliability engineering, and emerging cross-platform agent interoperability. It does not require access to proprietary model weights or hidden reasoning states and may operate through observable messages, structured proposed operations, tool interactions, controlled replay, and available processing-boundary evidence.
Technical significance: The disclosed approach moves beyond detecting incorrect AI-agent outcomes toward experimentally distinguishing supported causes, identifying observable communication-hop divergence, and preserving uncertainty where causal evidence is insufficient.
Keywords: Multi-agent AI; AI safety; instruction drift; semantic integrity; causal fault localization; counterfactual testing; adaptive witness selection; source authority; prompt-injection diagnosis; agent-to-agent communication; A2A; MCP; protocol-independent diagnostic adapters; TOCTOU; uncertainty-aware evaluation; distributed AI debugging; Google Gemini; Anthropic Claude; OpenAI Agents SDK; Microsoft Copilot Studio; Agent 365; Amazon Bedrock AgentCore; enterprise agent observability; agent evaluation.
Industry Mapping Disclaimer: Company and product references are AI-assisted technical relevance assessments illustrating potential integration environments only. They do not establish implementation, endorsement, affiliation, licensing, infringement, or confirmed future product-roadmap alignment. Technical comparisons should be independently verified against official company documentation. This abstract does not establish patent-filing or patent-pending status.
Creative Commons License
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Recommended Citation
Das, Sangam Kumar, "When AI Agents Change Instructions: Causal Detection of Semantic Drift and Communication-Hop Failures Using Counterfactual Witnesses and Protocol-Independent Diagnostics", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/12934