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Agentic architectures that ensemble specialized AI agents offer modularity, parallel throughput, and fault isolation for complex workflows yet demand rigorous integration layers to connect large-language-model (LLM) reasoning with external tools in a reproducible, auditable manner. We present a comprehensive integration of Anthropic’s Model Context Protocol (MCP)—a transport-agnostic JSON-RPC 2.0 framework whose machine-readable schemas advertise typed capabilities and unified error codes—with LangGraph. This state-preserving orchestrator models agent workflows as directed graphs of concurrent nodes. An industrial-grade MCP server implemented in Python 3.11 enforces strict JSON-Schema validation, cryptographic authentication, and exponential back-off retries. It is deployed as a Helm-packaged container for bit-for-bit reproducibility. The stack is evaluated in four production-realistic scenarios on a homogeneous Kubernetes cluster. First, an enterprise knowledge-base assistant couples an LLM planner with a vector-search MCP resource to sustain sub-second P95 responses in the documents while isolating schema violations. Second, an autonomous data-analysis pipeline interleaves sandboxed Python evaluation with on-the-fly SVG charting, generating FAIR-compliant provenance bundles that ensure data are findable, accessible, interoperable, and reusable. Third, a cross-agent scheduling workflow integrates Google Agent-to-Agent delegation with vertical MCP tool calls to Microsoft Graph, upholding ISO 27001 segregation of duties and sub-second end-to-end latency under continuous soak testing. Finally, an automated pharmacovigilance pipeline streams PubMed abstracts, extracts clinical metrics, and compiles Periodic Safety Update Reports whose SHA-256-backed audit trail meets EMA Good Pharmacovigilance Practice and CFR Part 11 electronic records requirements. Across these cases, MCP-enabled graphs scale linearly until external services saturate, degrade gracefully under injected faults, and retain end-to-end traceability. All code artifacts, container charts, and telemetry dashboards are released to foster replication, establishing the LangGraph and MCP stack as a robust foundation for compliant, high-throughput multi-agent systems.

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Protocol-Driven Agentic Integration of LLMs: A Multi-tool Use Case

  • 会议论文

  • 发表日期：

    2026年

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  • Pınar Ersoy,
  • Mustafa Erşahin

摘要

Agentic architectures that ensemble specialized AI agents offer modularity, parallel throughput, and fault isolation for complex workflows yet demand rigorous integration layers to connect large-language-model (LLM) reasoning with external tools in a reproducible, auditable manner. We present a comprehensive integration of Anthropic’s Model Context Protocol (MCP)—a transport-agnostic JSON-RPC 2.0 framework whose machine-readable schemas advertise typed capabilities and unified error codes—with LangGraph. This state-preserving orchestrator models agent workflows as directed graphs of concurrent nodes. An industrial-grade MCP server implemented in Python 3.11 enforces strict JSON-Schema validation, cryptographic authentication, and exponential back-off retries. It is deployed as a Helm-packaged container for bit-for-bit reproducibility. The stack is evaluated in four production-realistic scenarios on a homogeneous Kubernetes cluster. First, an enterprise knowledge-base assistant couples an LLM planner with a vector-search MCP resource to sustain sub-second P95 responses in the documents while isolating schema violations. Second, an autonomous data-analysis pipeline interleaves sandboxed Python evaluation with on-the-fly SVG charting, generating FAIR-compliant provenance bundles that ensure data are findable, accessible, interoperable, and reusable. Third, a cross-agent scheduling workflow integrates Google Agent-to-Agent delegation with vertical MCP tool calls to Microsoft Graph, upholding ISO 27001 segregation of duties and sub-second end-to-end latency under continuous soak testing. Finally, an automated pharmacovigilance pipeline streams PubMed abstracts, extracts clinical metrics, and compiles Periodic Safety Update Reports whose SHA-256-backed audit trail meets EMA Good Pharmacovigilance Practice and CFR Part 11 electronic records requirements. Across these cases, MCP-enabled graphs scale linearly until external services saturate, degrade gracefully under injected faults, and retain end-to-end traceability. All code artifacts, container charts, and telemetry dashboards are released to foster replication, establishing the LangGraph and MCP stack as a robust foundation for compliant, high-throughput multi-agent systems.