agha-actor-model

license	Apache-2.0
name	agha-actor-model
description	Foundational concurrent computation model where actors communicate exclusively through asynchronous message passing
category	Research & Academic
tags	["actor-model","concurrency","distributed-systems","message-passing"]

SKILL.md — Agha Actor Model: Concurrent Agent System Design

Decision Points

Choosing Actor Creation Strategy Based on Task Type

IF task has sequential dependencies:
├─ Use customer pattern: create child with reply address
├─ Pass customer address to child as parameter
└─ Child sends result directly to customer (not parent)

IF task requires long computation:
├─ Create insensitive actor for computation
├─ Forward incoming messages to buffer actor
└─ Resume from buffer when computation completes

IF task needs dynamic resource allocation:
├─ Create resource manager actors on demand
├─ Pass capabilities (addresses) as message data
└─ No central registry - addresses flow through system

IF task has failure isolation requirements:
├─ Spawn supervised child actors for risky operations
├─ Supervisor detects failure via missing replies
└─ Replace failed actors without affecting others

IF composing existing agent systems:
├─ Verify interface preserves causal structure
├─ Test behavior under composition (not just isolation)
└─ Use message protocols as boundaries (not shared state)

Message Sending vs State Replacement Decision Tree

IF coordination needed with other agents:
├─ SEND messages (don't modify local state first)
├─ Include reply address if response expected
└─ Never assume message ordering

IF local computation needed:
├─ SPECIFY replacement behavior
├─ Encapsulate new state (don't expose internals)
└─ Ensure one-message-at-a-time processing

IF dynamic scaling needed:
├─ CREATE new actors with specific behaviors
├─ Pass necessary addresses to new actors
└─ No shared initialization state

Failure Modes

1. Central Orchestrator Anti-Pattern

Detection: If you see one actor routing all messages or holding all system state Symptoms: Single point of failure, bottleneck under load, infinite regression problem Fix: Decompose into community of actors, each knowing only local context, use capability routing

2. Synchronous Blocking Fallacy

Detection: If actors wait/block for responses instead of specifying replacement behavior Symptoms: Deadlock under load, hidden timing assumptions, reduced concurrency Fix: Model as request-reply message pairs, use customer pattern for dependencies, apply insensitive actor pattern

3. Shared State Contamination

Detection: If multiple actors read/write same data structure (even with locks) Symptoms: Race conditions, sequential bottlenecks, hidden global state Fix: Encapsulate state in single actor, use message passing for coordination, mutual exclusion is free

4. Output-Only Verification

Detection: If testing only compares final outputs without checking interaction patterns Symptoms: Brock-Ackerman anomaly - identical outputs but different composition behavior Fix: Verify causal structure preservation, test behavior under composition, use observation equivalence

5. Static Topology Assumption

Detection: If communication graph is fixed at startup with no runtime reconfiguration Symptoms: Cannot handle open systems, no dynamic resource management, brittle under change Fix: Treat addresses as first-class data, implement capability routing, support runtime topology changes

Worked Examples

Example 1: Task Decomposition with Customer Pattern

Scenario: Agent needs to process a complex request requiring sequential subtasks A → B → C, but must remain responsive to other messages.

Novice Approach:

receive request →
  block while calling subtask A
  block while calling subtask B  
  block while calling subtask C
  send final result

Expert Application of Actor Model:

receive request →
  create customer_BC actor with addresses for B, C, final recipient
  send subtask A request to A_processor with customer_BC as reply address
  specify replacement behavior: ready for next request

customer_BC receives A result →
  send result to B_processor with customer_C as reply address
  
customer_C receives B result →
  send result to C_processor with final_recipient as reply address

Key Decisions Made:

• Used customer pattern to avoid blocking
• Each step creates next customer in chain
• Original agent remains responsive throughout
• Failure in any subtask only affects that chain

Example 2: Failure Recovery with Supervision

Scenario: System needs to handle agent failures without cascading to whole system.

Novice Approach: Try-catch around agent calls, restart everything on failure.

Expert Application:

supervisor creates worker_actor →
  sends task to worker with reply timeout
  specifies replacement: "waiting_for_reply"

IF reply received within timeout →
  forward result to client
  specify replacement: "ready"

IF timeout expires →
  create new worker_actor (old one failed)
  resend task to new worker
  specify replacement: "waiting_for_reply"

Trade-offs Navigated:

• Supervision is separate concern from business logic
• Failed actors are isolated and replaced, not repaired
• Timeout detection vs guaranteed delivery balance
• No shared state between supervisor and workers

Quality Gates

• No actor blocks its message processing loop (insensitive actor pattern applied where needed)
• All coordination uses message passing (no shared mutable state between actors)
• Message delivery is guaranteed but ordering is not assumed
• Addresses are treated as first-class data for capability routing
• Each actor specifies replacement behavior for every message type
• Actor creation happens dynamically based on computation needs
• Failure isolation prevents cascading failures across actor boundaries
• System topology can reconfigure at runtime without central registry
• Composition behavior verified, not just isolated component behavior
• Synchronous operations modeled as request-reply message pairs

NOT-FOR Boundaries

This skill should NOT be used for:

• Simple sequential computations → use functional programming instead
• Systems where shared memory is physically required → use lock-based concurrency patterns
• Real-time systems with hard timing constraints → use synchronous message passing with formal timing analysis
• Mathematical computations without coordination → use pure functional approaches

Delegate to other skills when:

• Implementing specific actor frameworks → use platform-specific implementation guides
• Performance tuning actor systems → use [performance-optimization] skill
• Formal verification of actor properties → use [formal-methods] skill
• Database design for actor persistence → use [data-architecture] skill

Common misconceptions about scope:

• Actors are not just "objects with async methods" - they require the full 3-tuple response
• Actor model is not just for distributed systems - applies to any concurrent computation
• Not primarily a performance optimization - it's a correctness and compositionality framework