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Agentic AI & MCP — Goal 3

Goal 3 delivers agentic AI orchestration that lets researchers drive MESA through natural-language conversation with an AI assistant. The orchestration layer is built on the Model Context Protocol (MCP) and self-hosted LLM inference on Jetstream-2 GPUs.

Stack

Layer Technology
LLM serving vLLM on Jetstream-2 GPUs (and commercial API egress as buffer)
RAG LiteLLM router; project-specific retrieval over docs + Lakehouse catalog
Agent protocol Model Context Protocol (MCP) — stdio, SSE, and Streamable-HTTP transports
Sandbox Container-based per-request sandboxes for tool execution
Multi-agent Multi-agent orchestration patterns (specialist agents per domain)

MCP servers in the MESA ecosystem

MESA bundles five MCP servers. Each one wraps an existing CyVerse capability as a set of agent-callable tools:

Server Wraps Repo
mesa-mcp iRODS Data Store + AVU + OBO/OLS ontology lookup + DuckLake cyverse/mesa-mcp
mesa-ducklake AVU-history Lakehouse cyverse/mesa-ducklake
irods-mcp-server Generic iRODS access cyverse/irods-mcp-server
formation-mcp CyVerse Formation API — DE job orchestration cyverse-de/formation-mcp
terrain-mcp CyVerse Terrain API cyverse/terrain-mcp

See the MCP servers overview for a comparison matrix.

Agent ergonomics

MCP servers are designed to be composable so that one prompt may invoke tools from several servers. For example, a discovery → annotate → launch workflow could call:

sequenceDiagram
    actor U as Researcher
    participant Cl as MCP client
    participant Me as mesa-mcp
    participant Mu as mesa-ducklake
    participant Fo as formation-mcp
    U->>Cl: "Find sandy-desert soil samples from 2024 and launch QIIME2 on them"
    Cl->>Me: ds_search_files_by_avu (ENVO:00000446 + date)
    Me-->>Cl: 47 matching files
    Cl->>Mu: mesa_ducklake_get_avu_history (sample of 5)
    Mu-->>Cl: history summary
    Cl->>Fo: launch_app (QIIME2, inputs=47 files)
    Fo-->>Cl: analysis ID + monitor URL
    Cl-->>U: "Launched QIIME2 — track at <URL>"

Sandbox security

Tool calls execute inside per-request container sandboxes so that:

  • A malformed or adversarial prompt cannot escape the agent runtime to reach the host or sibling user sessions.
  • iRODS credentials never leave the MESA service boundary.
  • Network egress is constrained to the configured set of MCP transports.

The sandbox model is reviewed during the TrustedCI prototype security review in WBS 6.5 (Phase 2).

Performance targets

Metric Target
MCP / RAG median latency < 5 s on Jetstream-2 GPUs
Simultaneous LLM requests 8–12 sustained on the leveraged Jetstream-2 GPU allocation
Service availability ≥ 95% business hours

The prototype performance ceiling is set by the leveraged infrastructure (Jetstream-2). Larger simultaneous-request scale on a dedicated GPU cluster is deferred to the follow-on operations proposal.

Multi-agent orchestration patterns

MESA does not commit to a single multi-agent framework. The prototype investigates:

  • Specialist agents — one agent per domain (data discovery, analysis, reporting), each backed by a different MCP server.
  • Tool-use scaling — using LLMs that can plan multi-step workflows across MCP tools without explicit orchestration code.
  • Human-in-the-loop checkpoints — for any operation that writes data or launches paid compute, the agent pauses for confirmation.