aethershell 0.3.1

The world's first multi-agent shell with typed functional pipelines and multi-modal AI
Documentation 
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# Real-World Multi-Agent Coordination Example
# Demonstrates practical use of Syntax KB and AgenticBinary for agent collaboration

print("=== Multi-Agent Task Distribution System ===")
print("")

# Scenario: A coordinator agent distributes tasks to worker agents
# Workers learn the protocol, receive tasks, execute them, and report results

# Step 1: Worker agents learn the AgenticBinary protocol
print("Phase 1: Protocol Discovery")
print("---------------------------")

# Worker 1 queries available protocols
available_protocols = syntax_list("protocol")
print("Worker 1: Available protocols -")
print(available_protocols)
print("")

# Worker 1 retrieves AgenticBinary specification
ab_spec = syntax_get("ab")
print("Worker 1: Learning AgenticBinary protocol...")
print("  Protocol: " + ab_spec.name)
print("  16 opcodes available for coordination")
print("")

# Step 2: Workers signal readiness to coordinator
print("Phase 2: Agent Registration")
print("---------------------------")

worker1_ready = ab_encode("response", "ack", "worker1:ready")
worker2_ready = ab_encode("response", "ack", "worker2:ready")
worker3_ready = ab_encode("response", "ack", "worker3:ready")

print("Worker 1 → Coordinator:")
print(ab_decode(worker1_ready))
print("")

print("Worker 2 → Coordinator:")
print(ab_decode(worker2_ready))
print("")

print("Worker 3 → Coordinator:")
print(ab_decode(worker3_ready))
print("")

# Step 3: Coordinator delegates tasks
print("Phase 3: Task Delegation")
print("------------------------")

task1 = ab_encode("command", "delegate", "analyze_logs:server1")
task2 = ab_encode("command", "delegate", "backup_database:prod")
task3 = ab_encode("command", "delegate", "optimize_queries:cache")

print("Coordinator → Worker 1: DELEGATE")
decoded_task1 = ab_decode(task1)
print("  Task: " + decoded_task1.payload)
print("")

print("Coordinator → Worker 2: DELEGATE")
decoded_task2 = ab_decode(task2)
print("  Task: " + decoded_task2.payload)
print("")

print("Coordinator → Worker 3: DELEGATE")
decoded_task3 = ab_decode(task3)
print("  Task: " + decoded_task3.payload)
print("")

# Step 4: Workers acknowledge and execute
print("Phase 4: Task Execution")
print("-----------------------")

# Worker 1 acknowledges
w1_ack = ab_encode("response", "ack", "task_received:analyze_logs")
print("Worker 1 → Coordinator:")
print(ab_decode(w1_ack))
print("")

# Worker 1 starts execution
w1_exec = ab_encode("command", "exec", "started:analyze_logs")
print("Worker 1 → Coordinator:")
print(ab_decode(w1_exec))
print("")

# Worker 2 and 3 also acknowledge
w2_ack = ab_encode("response", "ack", "task_received:backup_database")
w3_ack = ab_encode("response", "ack", "task_received:optimize_queries")

print("Worker 2 & 3 acknowledged tasks")
print("")

# Step 5: Workers report progress
print("Phase 5: Progress Updates")
print("-------------------------")

# Worker 1 sends intermediate data
w1_progress = ab_encode("response", "data", "logs_analyzed:1247,errors:3")
print("Worker 1 → Coordinator: PROGRESS")
progress1 = ab_decode(w1_progress)
print("  " + progress1.payload)
print("")

# Worker 2 sends sync message
w2_sync = ab_encode("command", "sync", "backup:75%")
print("Worker 2 → Coordinator: SYNC")
sync2 = ab_decode(w2_sync)
print("  " + sync2.payload)
print("")

# Step 6: Inter-worker collaboration
print("Phase 6: Agent Collaboration")
print("-----------------------------")

# Worker 3 needs help from Worker 1
w3_collab = ab_encode("command", "collaborate", "worker1:need_log_analysis")
print("Worker 3 → Worker 1: COLLABORATE request")
collab_req = ab_decode(w3_collab)
print("  Request: " + collab_req.payload)
print("")

# Worker 1 shares knowledge
w1_learn = ab_encode("command", "learn", "log_patterns:high_cpu,memory_leak")
print("Worker 1 → Worker 3: LEARN")
learn_msg = ab_decode(w1_learn)
print("  Knowledge: " + learn_msg.payload)
print("")

# Step 7: Error handling
print("Phase 7: Error Handling")
print("-----------------------")

# Worker 2 encounters an error
w2_error = ab_encode("response", "error", "backup_failed:disk_full")
print("Worker 2 → Coordinator: ERROR")
error_msg = ab_decode(w2_error)
print("  Error: " + error_msg.payload)
print("")

# Coordinator reassigns task
recovery = ab_encode("command", "delegate", "backup_database:backup_server")
print("Coordinator → Worker 2: Reassignment")
print(ab_decode(recovery))
print("")

# Step 8: Final results
print("Phase 8: Task Completion")
print("------------------------")

# Workers send final results
w1_result = ab_encode("response", "data", "analysis_complete:report.json")
w2_result = ab_encode("response", "data", "backup_complete:backup-20251108.tar.gz")
w3_result = ab_encode("response", "data", "optimization_complete:saved_37ms")

print("Worker 1 → Coordinator: COMPLETE")
print("  " + ab_decode(w1_result).payload)
print("")

print("Worker 2 → Coordinator: COMPLETE")
print("  " + ab_decode(w2_result).payload)
print("")

print("Worker 3 → Coordinator: COMPLETE")
print("  " + ab_decode(w3_result).payload)
print("")

# Step 9: Coordinator reflects on coordination efficiency
print("Phase 9: Meta-Analysis")
print("----------------------")

coordinator_reflect = ab_encode("command", "reflect", "coordination_efficiency:92%,avg_task_time:45s")
print("Coordinator → System:")
reflection = ab_decode(coordinator_reflect)
print("  Reflection: " + reflection.payload)
print("")

# Step 10: System-level planning
print("Phase 10: Future Planning")
print("-------------------------")

# Coordinator plans improvements
plan_msg = ab_encode("command", "plan", "optimize_delegation:use_worker_capabilities,parallel_execution")
print("Coordinator → System:")
plan = ab_decode(plan_msg)
print("  Plan: " + plan.payload)
print("")

# Summary
print("=== Coordination Complete ===")
print("")
print("Message Statistics:")
print("  Total messages: 17")
print("  Opcodes used: DELEGATE, ACK, EXEC, DATA, SYNC, COLLABORATE, LEARN, ERROR, REFLECT, PLAN")
print("  Agents: 1 coordinator + 3 workers")
print("  Tasks completed: 3/3")
print("")
print("Benefits of AgenticBinary:")
print("  • Compact binary encoding (3-5x compression)")
print("  • Semantic opcodes (no ambiguity)")
print("  • Version-aware protocol evolution")
print("  • Error recovery built-in")
print("  • Meta-cognition support (REFLECT, PLAN)")
print("")
print("Syntax KB enabled:")
print("  • Protocol discovery without hardcoding")
print("  • Shared understanding across agents")
print("  • Runtime learning of new protocols")
print("  • Persistent knowledge storage")