Agentic Titan

Problem

Multi-agent AI systems face a scaling problem: frameworks that work for two agents on a laptop collapse when you need 100+ agents with safety constraints, topology awareness, and production-grade observability.^[1] Russell and Norvig's comprehensive treatment of intelligent agents defines the theoretical foundation, but most existing frameworks implement only the simplest case: single-agent loops with tool access. They are either too opinionated (locked to one LLM provider, one communication pattern) or too thin (no safety layer, no topology abstractions, no way to reason about agent interactions at scale).^[2]

The eight-organ system needed an orchestration framework that could handle diverse workloads — from simple pipeline chains to complex mesh networks — while maintaining safety invariants and supporting any LLM backend. Wooldridge's formalization of multi-agent coordination protocols identified the core challenge: agents must negotiate communication, delegation, and conflict resolution through shared protocols rather than ad hoc messaging.^[3] Minsky's "society of mind" thesis — that intelligence emerges from the interaction of many simple agents — provided the philosophical grounding. Nothing on the market fit all three requirements simultaneously.

Approach

Build a polymorphic, model-agnostic framework that separates topology (how agents communicate) from archetype (what agents do) from safety (what agents can't do).^[4] This three-axis design draws directly from the Gang of Four's principle of separating interface from implementation — here applied at the system level rather than the class level. You can independently swap topologies, archetypes, and safety policies without rewriting orchestration logic.^[5] Martin's Dependency Rule — that source code dependencies must point inward toward policies, not outward toward mechanisms — governs the layering: safety wraps archetype wraps topology, and no inner layer knows about the outer ones.

Architecture

┌─────────────────────────────────────────────┐
│              Agentic Titan Core              │
├──────────┬──────────────┬───────────────────┤
│ Topology │  Archetypes  │    Safety Layer    │
│ Engine   │  (22 types)  │                   │
├──────────┤              ├───────────────────┤
│Pipeline  │ Researcher   │ Sandboxed exec    │
│Fan-out   │ Synthesizer  │ Resource limits   │
│Fan-in    │ Critic       │ Output validation │
│Mesh      │ Orchestrator │ Audit logging     │
│Hierarchy │ Specialist   │ Kill switches     │
│Ring      │ Guardian     │ Rate limiting     │
│Star      │ ... +16 more │ Circuit breakers  │
│Tree      │              │                   │
│Custom    │              │                   │
├──────────┴──────────────┴───────────────────┤
│         Model-Agnostic Interface            │
│   Anthropic · OpenAI · Ollama · Custom      │
├─────────────────────────────────────────────┤
│         Production Infrastructure           │
│ Prometheus · Structured Logs · Health Checks│
└─────────────────────────────────────────────┘

Nine Topology Patterns

Each topology encodes a different communication contract between agents.^[7] Tanenbaum and van Steen's classification of distributed system architectures — centralized, decentralized, and hybrid — maps directly onto these nine patterns. The topology engine enforces routing rules at the framework level so agents cannot violate their communication contracts.^[11]

from agentic_titan import Agent, Topology, SafetyPolicy

# Define agents by archetype
researcher = Agent(
    archetype="researcher",
    model="anthropic/claude-sonnet",
    config={
        "communication_style": "structured",
        "decision_pattern": "evidence_weighted",
        "error_strategy": "retry_with_backoff",
    }
)

critic = Agent(
    archetype="critic",
    model="openai/gpt-4",
    config={
        "communication_style": "adversarial",
        "decision_pattern": "falsification",
        "error_strategy": "escalate",
    }
)

# Connect via topology — agents don't know about each other
pipeline = Topology.pipeline([researcher, critic])

# Wrap in safety — mandatory, non-optional
policy = SafetyPolicy(
    sandbox=True,
    resource_limits={"max_tokens": 4096, "timeout_s": 30},
    output_validation=True,
    audit_logging=True,
)

# Deploy
result = pipeline.run(task="Analyze dataset", safety=policy)

Implementation

Built in Python 3.11+ across 18 development phases.^[9] McConnell's emphasis on construction quality — naming conventions, defensive programming, self-documenting code — shaped the implementation discipline across all 18 phases. The framework provides a unified interface where you define agents by archetype, connect them via topology, wrap them in safety policies, and deploy. Agent archetypes carry behavioral profiles (communication style, decision patterns, error handling strategies) that combine with topology constraints to produce emergent system behavior.^[12] Shoham and Leyton-Brown's formalization of agent interaction protocols — including mechanism design and social choice theory — informed how archetypes negotiate when their behavioral profiles conflict within a shared topology.

The safety layer is non-optional: every agent runs in a sandboxed context with configurable resource limits, output validation, and audit logging.^[8] Nygard's stability patterns — circuit breakers, bulkheads, timeouts — are implemented as first-class safety primitives rather than afterthoughts. Kill switches allow immediate agent termination. Circuit breakers prevent cascade failures in mesh and ring topologies. Every safety violation is logged with full context: which agent, which topology, which policy was breached, and what the agent attempted to do.

Results

1,095+ tests across the full test suite with production-hardened status.^[6] Fowler's discipline of continuous refactoring — improving internal structure without changing external behavior — kept the test suite stable across 18 development phases while the architecture evolved underneath. The framework is consumed downstream by ORGAN-III products: the Hunter Protocol in in-midst-my-life uses hierarchical topology for resume analysis, and the verification pipeline in The Actual News uses fan-out topology for parallel source checking. The governance patterns designed here (topology-as-constraint, safety-by-default) inform how ORGAN-IV manages the broader eight-organ system.

Tradeoffs & Lessons

• Topology abstraction vs. performance — The topology engine adds a routing layer that introduces latency (~2ms per hop). For latency-critical pipelines, direct agent-to-agent calls are faster. The tradeoff is worth it for complex topologies where reasoning about message flow matters more than raw speed.^[10] Brooks observed that conceptual integrity is the most important consideration in system design — the topology abstraction preserves conceptual integrity at the cost of raw throughput.
• 22 archetypes — too many? — Started with 6, grew to 22 as real use cases demanded finer behavioral distinctions. Some archetypes could be consolidated. The decision to keep them separate prioritizes clarity over minimalism.^[5]
• Model-agnostic tax — Supporting every LLM backend means maintaining adapter code for each. The unified interface hides provider differences but the adapters need updating when APIs change. Worth it for avoiding vendor lock-in.^[4] The Adapter pattern from the Gang of Four formalizes exactly this tradeoff: indirection cost vs. decoupling benefit.
• Safety overhead — Mandatory safety layer adds 5-10% overhead. This is intentional — unsafe agent systems are worse than slow ones.^[8] Nygard's principle that production-ready software must defend itself from its own components applies doubly to autonomous agent systems.

By the Numbers