Abstract

AI agents increasingly call tools asynchronously: a request returns a task handle, and the real outcome arrives later through polling, push notifications or webhooks. This often happens after connection loss, server failover, tool-catalog updates, cancellation, retries and concurrent quota use. In practice, a “completed” task status, a delivered callback, a subscription notification or a billing receipt is often treated as proof that the intended action happened, on the intended resource, under the intended schema and within budget. None of these signals establishes that on its own.

This disclosure describes a vendor-neutral method for reconstructing, from independently sourced and provenance-qualified evidence, whether an asynchronous tool outcome actually corresponds to the original request. The method:

  • binds every task, callback and notification to a generation-qualified original invocation;
  • determines the tool schema actually in force at execution time, not the latest catalog entry;
  • evaluates push events against the subscription filter the server acknowledged, not the one requested;
  • resolves resource identifiers by generation to detect reassignment;
  • reconstructs reservations, commits, retries, duplicate postings and credits per attempt to detect concurrent budget overruns.

Predicates are combined with three-valued (strong Kleene) logic under an explicitly declared verification objective, giving CONSISTENT, INCONSISTENT or UNRESOLVED. Missing evidence never becomes silent success. The result is read-only and never an authorization to execute, retry, cancel or charge.

The disclosure provides:

  • a 48-stage, six-phase workflow;
  • six embodiments;
  • 70 formal relations;
  • 40 checkpoints;
  • a comparison with MCP Tasks, signed webhooks, idempotency keys, durable workflow engines, distributed tracing, resource versioning, schema registries, quota systems and saga compensation;
  • a laboratory in Python, JavaScript and Go with 71 shared test vectors, all passing (about 0.4 to 2.3 microseconds per in-memory evaluation). The laboratory is synthetic, self-authored and not peer reviewed.

A companion industry-relevance section aligns the method with public roadmaps:

  • Amazon Web Services: MCP Tasks, Amazon Bedrock AgentCore and AWS Step Functions.
  • Microsoft: MCP tools for Azure AI Foundry, Agent 365 and Azure Durable Functions.
  • Other global platforms: Google Cloud, OpenAI, Salesforce, SAP and ServiceNow.
  • Chinese cloud and agent platforms: Alibaba Cloud, ByteDance Volcano Engine, Huawei Cloud, Tencent Cloud and Baidu AI Cloud.

Disclaimer: The industry and product-roadmap relevance is an AI-generated alignment based only on public announcements, documentation and market reports. It is not an endorsement, partnership, sponsorship, licence, technical validation or evaluation by any company or standards body named. It does not state or imply that any company uses, needs, has assessed or infringes this method or any patent application. All company, product and protocol names are trademarks of their respective owners and are used only to describe technical relevance. Patent pending: Indian Patent Application No. 202631129178 (source disclosure on Zenodo, record 23793543). Related PCT application: PCT/IB2026/055615, filed 4 June 2026 and publicly disclosed 16 July 2026. Readers must read the original disclosures before drawing any conclusion. The inventor is open to strategic collaboration or acquihire discussions.

Keywords

AI agent safety, asynchronous tool calls, Model Context Protocol, MCP Tasks, durable tasks, callback correlation, webhooks, idempotency, tool schema migration, list_changed, subscription filters, push notifications, resource versioning, alias reassignment, usage settlement, quota reservation, metered billing, three-valued logic, evidence provenance, TOCTOU, execution-effect verification, Amazon Bedrock AgentCore, AWS Step Functions, Azure AI Foundry, Agent 365, Azure Durable Functions, Google Cloud, OpenAI, Alibaba Cloud, Volcano Engine, Huawei Cloud, Tencent Cloud, Baidu AI Cloud

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License

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