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Agent Runtime Interop

Rhaios is a vertical yield layer. It focuses on yield strategy, execution preparation, and yield-specific status/history reads. Many agent platforms are horizontal runtime layers. They usually handle session lifecycle, general compute, wallet orchestration, and cross-tool workflows.

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Transport vs value

  • MCP is the transport: how runtimes discover and call Rhaios tools.
  • Rhaios is the execution engine: deterministic strategy selection, execution plan construction, validation, and relay for yield operations.
Do not treat MCP support itself as the product moat. The moat is trusted financial execution quality under autonomous agent workflows.

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Responsibility split

LayerOwns
Agent runtime (horizontal)Session management, orchestration, retries, wallet UX, payment plumbing
Rhaios (vertical)Yield strategy (yield_prepare), execution relay (yield_execute), portfolio/status reads (yield_status, yield_history)
  1. Runtime opens and manages MCP session to Rhaios (/mcp).
  2. Runtime sends yield_prepare for deposit/redeem/rebalance.
  3. Runtime wallet layer signs payload locally.
  4. Runtime submits signed payload with yield_execute.
  5. Runtime reads yield_status / yield_history for monitoring and agent memory.

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Security boundary

Rhaios is non-custodial:
  1. Rhaios does not hold signing keys.
  2. Rhaios does not sign transactions.
  3. Rhaios does not take custody of funds.
Signing authority remains in your wallet layer.

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Failure modes to handle in runtime layer

  1. Payment failures (402) for yield_prepare.
  2. Session lifecycle issues (mcp-session-id missing/expired).
  3. Transaction validation failures in yield_execute.
  4. RPC/transient relay failures requiring controlled retries.

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Practical guidance

  1. Treat yield_prepare responses as short-lived execution plans.
  2. Pass intentEnvelope.merkleRoot as intentId into yield_execute for explicit correlation.
  3. Keep user-facing retry policy in the runtime layer, not in prompt logic.
  4. Use /health for operational checks before large batches.