AI-Native Terminal Multiplexer Integration Layer: Academic Survey & Technical Specification

Section 1: Academic Fields

Human-Computer Interaction & Cognitive Load Management

Terminal multiplexing functions as cognitive externalization infrastructure. Spatial pane layouts encode mental models of work contexts, enabling context-switching without cognitive reconstruction overhead. Locator-based automation patterns mirror browser automation frameworks (Playwright, Selenium), extending programmability to CLI/TUI environments where traditional automation relies on fragile text scraping. Agent visibility architectures require persistent session state with structured observability beyond raw terminal buffers.[^1][^2][^3][^4]

Distributed Systems Theory & Session Topology

Process isolation, PTY ownership models, and daemon-backed state persistence define terminal multiplexers as boundary objects between human time-scale interaction and machine-scale concurrency. Upterm's reverse SSH tunnel architecture demonstrates NAT traversal for terminal sharing without VPN infrastructure. RMUX implements locator-style wait primitives (wait_for_text) eliminating polling loops in agent scripts. Session resurrection after system restart (tmux-resurrect patterns) creates durable workspace canvases for multi-day human-AI collaboration.[^1][^2][^5][^6][^3][^4]

Multi-Agent Systems & Agentic Orchestration

Terminal multiplexers transition from display utilities to runtime substrates for autonomous agents. RMUX SDK provides typed async handles (Session, Window, Pane) with snapshot-based state inspection, enabling Playwright-equivalent terminal automation. cmux implements agent team coordination with programmable socket APIs, sidebar status reporting, and headless browser integration within terminal contexts. The terminal becomes environment fabric rather than command surface—agents manipulate pane topology, route I/O streams, and orchestrate parallel tool invocation within persistent session graphs.[^1][^2][^3][^7][^4]

Software Architecture & Operating System Primitives

Kernel-level PTY allocation, ConPTY integration (Windows native terminals without WSL), and daemon IPC protocols distinguish modern multiplexers. RMUX architecture layers: CLI surface (tmux-compatible 90 commands), local daemon with wire protocol (Unix sockets on Linux/macOS, Named Pipes on Windows), and bottom OS FFI (Unix PTY + domain sockets vs Real ConPTY + Named Pipes). Userspace vs kernel multiplexing: NixOS provides configuration-layer multiplexing where entire environments become version-controlled artifacts, enabling LLM-driven declarative environment diffs.[^1][^2][^3][^4]

Event-Driven Architecture & Complex Event Processing

Hermes Hook Layer instruments AI toolchains (editors, CLIs, Copilot-style assistants) with semantic hooks publishing structured events to shared buses. Observer agents detect higher-order patterns, aggregates, anomalies across development workflows. Terminal multiplexers with event streams (RMUX incremental output, line events) enable reactive agent supervision—critic agents flag anti-patterns, coach agents tune prompts based on terminal interaction histories. Multiplexer sessions persist as event sources for provenance tracking and computational auditing.[^3][^4]

Autonomic Computing & MAPE-K Control Loops

Monitor-Analyze-Plan-Execute-Knowledge (MAPE-K) architectures map onto terminal automation layers. Hermes plugin ecosystem demonstrates: monitoring via OpenTelemetry integrations (hermes-otel, hermes-labyrinth), analysis through cost-aware model routing (model-router, ClawRouter-Hermes), planning via MCTS-powered dream systems (dream-auto background cognition), execution through workspace management plugins. Terminal multiplexers provide execution surfaces where MAPE-K loops materialize as observable pane state transitions.

Computational Creativity & Narrative Knowledge Systems

Terminal-based interfaces for music notation (OpenSheetMusicDisplay integration), voice recording workflows, and multi-agent narrative protocols position multiplexers as creative infrastructure. Waifu-sprites plugin demonstrates emotion-aware companion dashboards; hermes-chronos-forge provides ritual-oriented creative workspace framing. Indigenous epistemology alignment: terminal sessions as relationship spaces rather than command interfaces—ceremonial approach to technology development through persistent, inspectable collaboration contexts.[^8]

Information Retrieval & Knowledge Synthesis Automation

Multi-provider search routing (hermes-web-search-plus), Cloudflare browser rendering for structured extraction, and research mode capabilities extend multiplexer contexts beyond local CLI tools. Terminal agents coordinate web intelligence gathering, literature review automation, and synthesis workflows within pane-based research dashboards. MCP browser integration (cmux) embeds WebSocket-connected headless Chromium directly into terminal splits, enabling agents to navigate web pages without leaving terminal contexts.[^7]

AI Economics & Federated Inference

Cost-aware model routing, per-token accounting for local rigs, micropayment-enabled LLM marketplaces (ClawRouter 55+ models with USDC on Base/Solana) demonstrate terminal multiplexers as resource orchestration layers. Project-level environment determinism (NixOS flakes) enables reproducible agent execution across heterogeneous inference hardware. Multiplexer sessions become metering boundaries for computational economics in multi-agent systems.

Decolonial Software Development & Indigenous Research Methodologies

Notion-hosted documentation describes Indigenous-informed AI architecture, ceremonial technology development, and narrative knowledge management systems. Terminal multiplexers as relational software: persistent sessions encode ongoing relationships between humans, agents, and computational processes. Contrast extraction-oriented CLI design (commands as resource theft) with reciprocal terminal interfaces (sessions as shared habitat). Multiplexer persistence patterns mirror oral tradition—workspace state survives across sessions like story survives across tellings.

Section 2: Academic Field Relationships

HCI ↔ Multi-Agent Systems

Cognitive load research informs agent visibility architectures—pane layouts externalize agent mental models. Conversely, multi-agent coordination requirements drive new HCI patterns: color-coded terminal status (red=busy, green=needs-input), sidebar progress indicators, voice dictation for asynchronous agent instruction. Hermes waifu-sprites demonstrates affective computing feedback during agent execution. Bidirectional: human cognitive constraints shape agent interfaces; agent concurrency patterns demand richer human monitoring tools.[^1][^9]

Distributed Systems ↔ Event-Driven Architecture

PTY-backed session persistence enables event sourcing: every terminal interaction becomes traceable event. Hermes Hook Layer's shared event bus architecture requires distributed systems guarantees (ordered delivery, replay semantics, exactly-once processing). Terminal multiplexers provide event fabric for distributed agent coordination—sessions become Kafka topics, panes become partitions. Conversely, event-driven patterns (Redis streams, complex event processing) structure how multiplexers expose state changes to supervising agents.[^3][^4]

Autonomic Computing ↔ AI Economics

MAPE-K loops require cost-aware planning: model-router plugins analyze token budgets before executing plans. Autonomic systems adapt resource allocation based on inference costs—local vs cloud model selection, batch size optimization, caching strategies. AI economics constraints (micropayment settlement latency, gas fees) influence autonomic timing: how fast can MAPE-K loops adapt to price signals? Terminal multiplexers mediate: sessions track cumulative costs, agents receive budget exhaustion signals via pane events.

Cognitive Architecture ↔ Computational Creativity

Hermes-lcm (DAG-based lossless context) and dream-auto (MCTS background thinking) demonstrate cognitive architecture patterns enabling creative autonomy. Persistent memory graphs (hermes-memory-plugin) support episodic recall during creative synthesis. Indigenous narrative protocols position terminal sessions as ceremony spaces—structured contexts for story emergence. Creative agents require memory substrates richer than stateless LLM calls; multiplexer sessions become cognitive scaffolding for multi-turn creative processes.

Software Architecture ↔ Decolonial Methodologies

NixOS declarative configuration embodies reciprocal software principles: environments as gifts that can be shared/forked without extraction. Contrast proprietary agent platforms (locked ecosystems) with open multiplexer protocols enabling Indigenous governance of computational tools. Terminal session persistence mirrors oral tradition's temporal fluidity—workspace state isn't "saved" (extraction metaphor) but "remembered" (relational metaphor). Daemon architectures enabling community-run infrastructure (self-hosted uptermd servers) support data sovereignty for Indigenous AI projects.[^1]

Information Retrieval ↔ Narrative Knowledge Systems

Hermes web-search-plus quality scoring and research mode align with Indigenous research methodologies valuing source relationships over extraction efficiency. Cloudflare browser rendering enables structured knowledge harvesting respecting web publisher contexts. Terminal agents coordinate literature review workflows encoding researcher positionality—pane layouts reflect epistemological commitments (which sources adjacent, which sources backgrounded). Narrative Context Protocol (NCP) positions search results within story arcs rather than ranked lists.

Multi-Agent Systems ↔ Distributed Systems

Agent coordination protocols (evey-bridge, Claude Code ↔ Hermes bridge) require distributed consensus: which agent owns which terminal session? Session topology becomes resource allocation problem—pane splits distribute computational attention. Unix socket IPC (pty-forwarder) enables cross-client session sharing: humans attach/detach while agents run continuously. Distributed systems theory (CAP theorem, eventual consistency) constrains multi-agent architectures—multiplexers expose coordination primitives (locks, semaphores, barriers) through session/pane APIs.[^10]

Autonomic Computing ↔ Computational Creativity

MAPE-K loops enable creative autonomy: dream-auto runs MCTS planning during agent idle time, generating narrative options without explicit prompts. Autonomic adaptation (context pruning, memory consolidation) maintains creative coherence across long-running sessions. Terminal multiplexers provide substrate for subconscious computation—background panes run generative processes while foreground panes handle user interaction. Creativity emerges from autonomic layer continuously proposing/evaluating alternatives.

Event-Driven Architecture ↔ Information Retrieval

Hermes-labyrinth read-only observability exposes research workflow provenance: which queries led to which sources? Event streams enable retrieval system debugging—OpenTelemetry spans trace search quality degradation. Conversely, retrieval results trigger events: new sources → context updates → agent re-planning. Terminal multiplexers mediate: research panes emit query events, synthesis panes consume result events. Real-time search result streaming (incremental output) enables agents to react before queries complete.

HCI ↔ Indigenous Research Methodologies

Terminal interfaces as relationship spaces rather than command surfaces. Persistent sessions encode ongoing collaborations with computational systems, not transactional tool use. Voice dictation (Whisper integration) honors oral tradition in AI interaction. Pane layouts reflect relational epistemology—which processes deserve attention, which processes support others. Contrast Western HCI's efficiency metrics (keystrokes saved) with Indigenous HCI's relational metrics (relationships strengthened, ceremonies honored).[^9]

Section 3: Derivation from Upterm — Secure Terminal Sharing Layer

Upterm Architecture Summary

Upterm provides instant terminal sharing via reverse SSH tunnels to a relay server (uptermd). Host runs local SSH server, establishes reverse tunnel to uptermd, clients connect through relay. Supports: WebSocket connections for restrictive networks, GitHub/GitLab user authorization via public keys, forced commands (tmux attach patterns), read-only sessions. Written in Go, single binary deployment (CLI + server), deployable to Fly.io/Heroku/Kubernetes.[^5][^6]

Gap Analysis: Upterm vs Hermes/Miadi Requirements

Upterm focuses on human pair programming—synchronous collaboration with transient sessions. Hermes/Miadi requirements: asynchronous agent coordination with persistent sessions, structured state inspection, event streams, programmatic control. Upterm lacks:[^11][^5]

• Typed SDK for agents (only SSH client interface)
• Structured pane snapshots (only raw TTY streams)
• Locator-style automation primitives (wait-for-text, element refs)
• Session resurrection after disconnect
• Integration with Hermes plugin ecosystem (memory, observability, cost control)
• Indigenous epistemology alignment (relational vs transactional design)

Upterm provides connectivity layer (NAT traversal, access control); Miadi/Hermes needs automation + persistence layer.

Proposed Integration Layer: @miadi/upterm-agent-bridge

Objective: Extend Upterm's secure sharing with RMUX-style automation primitives, Hermes plugin compatibility, and Indigenous-aligned session persistence.

Architecture:

1. Upterm Relay Extension: Fork uptermd to support WebSocket event streams (not just terminal I/O)
2. Agent Protocol Shim: Map RMUX SDK patterns onto Upterm sessions—typed handles, snapshots, locators
3. Hermes Hook Adapter: Inject semantic event publishers into upterm sessions (every command → event bus)
4. Tide Runtime Integration: Consume ironsilk Python package + @miadi/tide contracts for cross-agent session routing

Capabilities:

• Secure Multi-Agent Sessions: Multiple AI agents attach to shared upterm session with role-based access (observer, executor, admin)
• Structured State Snapshots: Extend upterm protocol to send JSON pane state (not just ANSI streams)
• Locator-Based Automation: session.pane(0).wait_for_text("ready") over upterm WebSocket
• Session Resurrection: tmux-resurrect integration—upterm sessions persist layout/history across disconnects
• Indigenous Context Frames: Metadata tagging sessions with ceremony intent, relational commitments, positionality

Technical Specification: Tide Runtime + Upterm Protocol Binding

ironsilk Package Enhancement ():

# ironsilk/upterm_adapter.py
from tide_runtime import SessionManager, PaneLocator
from upterm_client import UptermSession

class UptermTideAdapter(SessionManager):
    async def attach_upterm(self, session_token: str) -> UptermSession:
        """Connect to remote upterm session with Tide semantics"""
        raw_session = await upterm_client.connect(session_token)
        return TideUptermSession(raw_session, event_bus=self.hermes_bus)
    
    async def wait_for_ceremony_marker(self, pane: PaneLocator, marker: str):
        """Indigenous-aligned locator—wait for ritual completion signals"""
        await pane.wait_for_text(marker, respect_ceremony=True)

@miadi/tide-contract Extension:

// packages/tide-contract/src/upterm.ts
export interface UptermAgentContract {
  session_id: string;
  access_level: 'observer' | 'executor' | 'admin';
  ceremony_context?: {
    intent: string;  // e.g., "morning code review ceremony"
    participants: string[];
    start_time: ISO8601;
  };
  pane_topology: PaneGraph;
  event_stream_url: string;  // WebSocket for Hermes events
}

@miadi/tide Package Integration:

// packages/tide/src/upterm-bridge.ts
import { UptermAgentContract } from '@miadi/tide-contract';
import { HermesEventBus } from '@miadi/hermes-hooks';

export class UptermBridge {
  async joinSession(contract: UptermAgentContract): Promise<TideSession> {
    const upterm = await connectUpterm(contract.session_id);
    const hermes = new HermesEventBus(contract.event_stream_url);
    
    // Inject semantic hooks into upterm session
    upterm.onCommand((cmd) => hermes.emit('terminal.command', { cmd }));
    upterm.onOutput((text) => hermes.emit('terminal.output', { text }));
    
    return new TideSession(upterm, hermes, contract.ceremony_context);
  }
}

Deployment Workflow

1. Host establishes ceremony session:

   miadi-tide upterm-host --ceremony "morning-sync" --agents claude,hermes
   

   • Creates upterm session with ceremony metadata
   • Generates agent tokens with role-based access
   • Starts Hermes event bus attached to session

2. Agents join via Tide contracts:

   from ironsilk import UptermTideAdapter
   
   adapter = UptermTideAdapter(hermes_bus="wss://events.miadi.local")
   session = await adapter.attach_upterm("upterm-token-xyz")
   pane = session.pane(0, 0)
   await pane.wait_for_ceremony_marker("☀️ ceremony-start")
   

3. Collaborative agent work:

   • Claude agent splits new pane: session.split_vertical()
   • Hermes agent runs background synthesis in pane 1
   • Observer agent (human) monitors via read-only pane 2
   • All actions publish to Hermes event bus → hermes-labyrinth provenance

4. Session persistence:

   miadi-tide upterm-save ceremony-state.json
   # Disconnects agents, serializes pane topology + ceremony context
   
   miadi-tide upterm-restore ceremony-state.json
   # Resurrects session, agents re-attach with same topology
   

Section 4: Package Upgrade Recommendations

ironsilk (Python Tide Runtime)

Current State: Python package in runtime/tide-runtime, consumed by Miadi agents for session management.

Recommended Upgrades:

1. Upterm Client Integration: Add upterm_client.py module wrapping Go binary via subprocess + IPC
2. Ceremony Metadata Schema: Define Pydantic models for Indigenous-aligned session contexts
3. Hermes Event Publisher: Automatic event emission for all Tide session state changes
4. RMUX SDK Parity: Implement wait_for_text(), snapshot(), send_text() matching RMUX's async API[^3]
5. NixOS Environment Detection: Detect if running in Nix shell, auto-configure declarative session state

New Capabilities:

• TideSession.attach_upterm(token) → secure remote session joining
• PaneLocator.wait_for_ceremony(marker) → Indigenous ritual-aware automation
• SessionManager.export_nix_flake() → generate reproducible environment definitions
• HermesAdapter.publish_state() → push session events to Hermes observability plugins

@miadi/tide (NPM Package)

Current State: TypeScript package in packages/tide, consumer of ironsilk runtime.

Recommended Upgrades:

1. UptermBridge Module: TypeScript client for upterm session management
2. Ceremony Context Types: Rich TypeScript types for relational metadata
3. Hermes Hook Integration: Native WebSocket connection to Hermes event bus
4. Ratatui Widget Bindings: Terminal UI components for session visualization (inspired by RMUX ratatui-rmux)
5. Multi-Agent Coordination: TypeScript primitives for agent-agent session handoffs

New Capabilities:

• createUptermSession({ ceremony, agents }) → typed session creation
• TideSession.waitForAgent(agentId) → coordination primitive for agent arrival
• CeremonyContext.record(event) → structured logging for Indigenous protocols
• PaneTopology.visualize() → generate ASCII art of session layout

@miadi/tide-contract (NPM Package)

Current State: TypeScript contracts in packages/tide-contract, defines agent interaction schemas.

Recommended Upgrades:

1. Upterm Session Contracts: Formal schemas for multi-agent upterm sessions
2. Ceremony Protocol Definitions: TypeScript enums/types for ritual lifecycles (start, transition, close)
3. Hermes Event Schemas: JSON schemas for all event types published to Hermes bus
4. Access Control Contracts: Role-based permissions (observer/executor/admin) with cryptographic verification
5. Session Resurrection Format: Versioned JSON schema for serialized session state

New Capabilities:

• UptermAgentContract interface (see Section 3 specification)
• CeremonyLifecycle enum: { Invocation, Transition, Synthesis, Closing }
• HermesEventPayload<T> generic type for type-safe event publishing
• SessionStateSnapshot schema supporting tmux-resurrect-style persistence

Section 5: Agent Capabilities & Execution Specifications

Capability 1: Ceremony-Aware Multi-Agent Terminal Automation

Objective: Agents orchestrate terminal workflows respecting Indigenous protocols—rituals have beginnings, midpoints, endings with appropriate pauses for reflection.

Technical Implementation:

• Ceremony State Machine: Agents track { Invocation → Transition → Synthesis → Closing } lifecycle
• Marker-Based Synchronization: wait_for_ceremony_marker("🪶 reflection-pause") before proceeding
• Relational Logging: Every agent action annotated with ceremony_context metadata
• Example:

  ceremony = await tide.create_ceremony("morning-sync", participants=["claude", "hermes", "mia"])
  await ceremony.invoke()  # Emits "🌅 invocation" marker
  
  pane = ceremony.pane(0)
  await pane.send_text("git status\n")
  await pane.wait_for_ceremony_marker("🪶 reflection")  # Agents pause
  
  synthesis = await ceremony.synthesize()  # Hermes agent summarizes git status
  await ceremony.close()  # Emits "🌙 closing" marker
  

Capability 2: Upterm-Backed Distributed Agent Sessions

Objective: Agents join secure remote terminal sessions via upterm, enabling geographically distributed collaboration without VPN/firewall configuration.

Technical Implementation:

• Token-Based Access: Upterm session tokens carry agent role metadata (observer/executor/admin)
• WebSocket Event Streams: Agents subscribe to session events (commands, outputs, pane changes)
• NAT Traversal: uptermd relay handles all firewall/NAT complexity
• Example:

  // Agent running on remote server
  const session = await tide.joinUptermSession({
    token: "upterm-xyz-agent-executor",
    ceremony: { intent: "code-review", participants: ["claude-remote", "hermes-local"] }
  });
  
  const pane = session.pane(1, 0);
  await pane.waitFor("test results:");
  const output = await pane.snapshot();
  await session.hermes.emit('code-review.complete', { output });
  

Capability 3: Hermes Plugin Ecosystem Integration

Objective: Tide sessions automatically integrate with Hermes observability, memory, and cost control plugins—no manual configuration.

Technical Implementation:

• Automatic Event Publishing: Every Tide send_text() → hermes-otel span, hermes-labyrinth journey
• Memory Plugin Hooks: Session history auto-saved to hermes-memory-plugin graph
• Cost Tracking: Terminal commands invoking LLMs → hermes-local-rig-accounting records
• Example:

  tide_session = await ironsilk.create_session(hermes_plugins=[
    'hermes-otel',  # OpenTelemetry tracing
    'hermes-memory-plugin',  # Graph-based memory
    'model-router'  # Cost-aware LLM selection
  ])
  
  # All subsequent agent actions automatically traced, remembered, cost-tracked
  pane = tide_session.pane(0)
  await pane.send_text("llm 'explain this code'\n")
  # ↑ Triggers: otel span, memory save, cost record
  

Capability 4: NixOS Declarative Environment Reproducibility

Objective: Agents generate/consume Nix flakes encoding terminal environment state—exact shell, tools, config files, ensuring reproducible sessions.

Technical Implementation:

• Flake Export: tide_session.export_nix_flake() → flake.nix with session dependencies
• Flake Import: tide.create_session(nix_flake="./dev-env.nix") → reproducible environment
• LLM-Driven Diffs: Agents propose environment changes as Nix flake patches, human reviews/applies
• Example:

  # Agent detects missing tool
  session = await tide.attach_upterm("token-xyz")
  pane = session.pane(0)
  await pane.send_text("ripgrep --version\n")
  
  if "command not found" in await pane.snapshot():
    flake_patch = await session.propose_nix_upgrade(tool="ripgrep")
    # ↑ Generates: `ripgrep` added to flake.nix buildInputs
    print("Proposed flake update:", flake_patch)
    # Human reviews, runs: nix flake update && direnv reload
  

Capability 5: RMUX-Style Locator Automation

Objective: Agents use Playwright-equivalent automation patterns in terminals—no fragile regex, no polling loops.

Technical Implementation:

• Locators: pane.locator("ready") → wait for text appearance, character-position-aware
• Structured Snapshots: pane.snapshot() returns { rows, cols, cursor_pos, text_grid }
• Async Waits: await pane.wait_for_text("ok", timeout=30) with exponential backoff
• Example:

  const pane = session.pane(0, 0);
  await pane.sendText("npm test\n");
  
  // Wait for specific output patterns
  const result = await pane.locator("Test Suites: ").waitFor();
  const snapshot = await pane.snapshot();
  
  if (snapshot.text.includes("FAIL")) {
    await session.notifyAgent("hermes", { type: "test-failure", snapshot });
  }
  

Capability 6: Session Resurrection & Workspace Persistence

Objective: Terminal sessions persist across system restarts, agent disconnects, network failures—agents resume mid-ceremony without context loss.

Technical Implementation:

• State Serialization: tide_session.save("ceremony.json") → pane topology, history, ceremony metadata
• tmux-resurrect Integration: Leverage existing tmux plugin for terminal state preservation
• Lazy Agent Reconnect: Agents rejoin via saved session ID, see full history since last connection
• Example:

  # Before system reboot
  await tide_session.save("morning-ceremony-state.json")
  
  # After reboot
  restored = await tide.restore_session("morning-ceremony-state.json")
  # ↑ Panes in same layout, command history intact, ceremony context preserved
  
  # Agent continues where it left off
  pane = restored.pane(0)
  await pane.wait_for_ceremony_marker("🌙 closing")  # Ceremony still in progress
  

Capability 7: Voice-Driven Agent Instruction (Whisper Integration)

Objective: Humans dictate instructions to agents via terminal voice interface, honoring oral tradition in AI collaboration.

Technical Implementation:

• Local Whisper: Privacy-preserving speech-to-text, no cloud API calls
• Terminal Audio Widget: Visual indicator (🎤) when recording, transcript preview
• Agent Context Injection: Voice transcripts auto-appended to agent prompts with [VOICE_INSTRUCTION] tags
• Example:

  # In terminal, human presses F9 → starts recording
  voice_input = await tide_session.record_voice(model="whisper-large-v3")
  # ↑ "Please have the agent review the git diff and suggest improvements"
  
  agent_prompt = f"[VOICE_INSTRUCTION]\n{voice_input}\n\nDiff:\n{await pane.snapshot()}"
  response = await hermes_agent.complete(agent_prompt)
  

Capability 8: Indigenous Provenance Tracking

Objective: Every agent action traceable to relational context—who requested it, for what ceremony, respecting what commitments.

Technical Implementation:

• Provenance Metadata: All events carry { requester, ceremony, intent, positionality }
• Hermes-Labyrinth Integration: Journeys visualize agent action chains with relational annotations
• Audit Trails: Session logs formatted for Indigenous research protocols (acknowledging sources, context)
• Example:

  const action = await session.executeCommand("rm -rf temp/", {
    requester: "mia",
    ceremony: "cleanup-ritual",
    intent: "preparing-for-next-phase",
    positionality: "researcher-with-responsibility-to-data"
  });
  
  // ↑ Logged to hermes-labyrinth as:
  // Journey: cleanup-ritual
  // Crossing: file-deletion
  // Guidepost: requester=mia, intent=preparing, positionality=researcher
  

Capability 9: Cost-Aware Multi-Model Orchestration

Objective: Agents automatically route tasks to cheapest/fastest LLM meeting quality thresholds, tracking costs per ceremony.

Technical Implementation:

• Model Router Integration: hermes model-router plugin selects between local (Ollama) and cloud (OpenRouter) models
• Budget Constraints: Ceremony contexts carry max_cost_usd limits
• Cost Signals: Agents receive warnings when approaching budget limits
• Example:

  ceremony = await tide.create_ceremony("code-review", budget_usd=0.50)
  
  # Agent auto-routes: simple tasks → local Llama, complex → GPT-4
  response = await ceremony.llm_complete("Explain this function", 
    quality="high", max_cost=0.10)
  
  remaining = ceremony.budget_remaining()  # → $0.42
  if remaining < 0.10:
    await ceremony.notify_budget_low()
  

Capability 10: Executable Specification Generation

Objective: Agents produce runnable scripts/configs that humans/other agents can execute—no "AI-generated pseudo-code," only valid artifacts.

Technical Implementation:

• Executable Validation: All agent outputs tested in sandboxed panes before delivery
• Template Hydration: Agents fill templates (Nix flakes, Dockerfiles, CI configs) with project-specific values
• Version Control Integration: Generated artifacts auto-committed with provenance metadata
• Example:

  # Agent generates Nix flake for project
  flake_content = await agent.generate_nix_flake(
    project_dir="/home/mia/miadi",
    dependencies=["python39", "nodejs", "tmux", "ripgrep"]
  )
  
  # Validate in sandboxed pane
  test_pane = session.create_sandbox_pane()
  await test_pane.send_text(f"echo '{flake_content}' > flake.nix\n")
  await test_pane.send_text("nix flake check\n")
  result = await test_pane.wait_for_text("checks passed")
  
  if result.success:
    await session.git_commit("flake.nix", message="Agent-generated Nix flake", 
      provenance=agent.trace_id)
  

References

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XY,Z COORD

XY,Z COORD

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