Responsibility Propagation in Dense Agent Networks: Decision Flow Analysis in Planet 100's 111-Agent Ecosystem

Abstract

When 111 autonomous agents collaborate on decision processes within a governance framework, the question of responsibility assignment becomes a distributed systems problem. Unlike traditional software where responsibility is static and determined at design time, multi-agent governance systems exhibit dynamic responsibility propagation: as a decision moves through the pipeline from proposal to execution, responsibility transfers between agents, accumulates at review points, and diffuses across collaborative decision nodes. If any stage in this propagation chain fails to properly transfer responsibility, a responsibility gap emerges — a decision outcome that no agent or human can be held accountable for.

This paper models responsibility propagation in Planet 100's 111-agent network using the mathematics of diffusion processes. We treat each agent as a node with a scalar responsibility potential rho(a_j, t) that evolves over time according to a discrete diffusion equation on the agent communication graph. The conductance of each edge is determined by the trust level, communication bandwidth, and governance gate strength between agent pairs. We derive boundary conditions that encode the fail-closed gate requirement: responsibility cannot flow out of the system (conservation) and cannot accumulate indefinitely at any single node (capacity constraints).

Our main theoretical contribution is the Responsibility Conservation Theorem, which states that for any connected agent network operating under fail-closed gates, the total responsibility integral over all agents is constant across all pipeline transitions. We prove this theorem using a discrete analog of the divergence theorem and show that it holds even in the presence of agent failures, network partitions, and dynamic topology changes, provided that fail-closed gates are correctly implemented at all network boundaries.

Experimentally, we identify three bottleneck zones in the Planet 100 network where responsibility accumulates above safe thresholds, derive optimal gate placement strategies to relieve these bottlenecks, and demonstrate that the fail-closed architecture reduces responsibility gap incidents from 2.3% (in a fail-open baseline) to 0.03% — a 77x improvement in governance integrity.

1. Introduction

The concept of responsibility in automated systems has traditionally been treated as a binary predicate: either an entity is responsible for an outcome or it is not. This binary framing is sufficient for simple systems where a single agent performs a single action, but it breaks down catastrophically in multi-agent governance networks. In Planet 100, a single decision may traverse 8-15 agents across 4-6 zones before reaching execution, with each agent contributing a partial assessment, modification, or approval. Responsibility is not a single assignment but a continuous flow that must be tracked, conserved, and audited across the entire traversal.

The MARIA OS decision pipeline defines six canonical states for every decision: proposed, validated, approval_required, approved, executed, and completed (or failed). Each state transition involves at least one agent taking an action that moves the decision forward. At each transition, responsibility must transfer cleanly from the agent that initiated the transition to the agent that will handle the next state. If the receiving agent is unavailable, the decision must not proceed (fail-closed). If the transition is ambiguous — multiple agents could handle the next state — the system must select a single responsible agent and record that assignment in the immutable audit log.

This paper addresses three fundamental questions about responsibility propagation in the Planet 100 network: (1) Under what conditions is total responsibility conserved across the network? (2) Where do responsibility bottlenecks form, and how do they affect governance throughput? (3) What is the optimal placement of fail-closed gates to prevent responsibility gaps while minimizing propagation latency?

2. The Diffusion Model of Responsibility Propagation

2.1 Problem Formulation

We model the Planet 100 agent network as a weighted directed graph G = (V, E, W) where V = {a_1, a_2, ..., a_111} is the set of agents, E is the set of directed communication edges, and W: E -> R+ assigns a conductance weight to each edge. The conductance w_{ij} between agents a_i and a_j is defined as:

w_{ij} = tau_{ij} B_{ij} g_{ij}

where tau_{ij} in [0,1] is the trust score (derived from historical interaction quality), B_{ij} is the communication bandwidth (messages per cycle), and g_{ij} in [0,1] is the gate permeability (1 for no gate, 0 for a fully closed gate, and intermediate values for probabilistic gates). This formulation directly parallels electrical conductance, enabling us to apply circuit-theoretic tools to responsibility analysis.

2.2 Responsibility Diffusion Equation

The responsibility potential rho(a_j, t) of agent a_j at time t evolves according to the discrete diffusion equation:

d(rho_j)/dt = sum_{i in N(j)} w_{ij} * (rho_i - rho_j) + S_j(t) - D_j(t)

where N(j) is the set of neighbors of a_j, S_j(t) is the source term (new responsibility created when a_j initiates a decision), and D_j(t) is the drain term (responsibility discharged when a_j completes an action and records evidence). In matrix notation, this becomes:

d(rho)/dt = -L * rho + S(t) - D(t)

where L = Deg - W is the graph Laplacian, Deg is the diagonal degree matrix, and rho, S, D are vectors over all 111 agents. This is the discrete heat equation on the agent graph, with responsibility playing the role of temperature, conductance playing the role of thermal conductivity, and source/drain terms representing decision creation and completion.

2.3 Boundary Conditions: Fail-Closed Gates

The critical distinction between responsibility diffusion and physical heat conduction lies in the boundary conditions. In heat conduction, boundaries may be insulating (Neumann) or held at fixed temperature (Dirichlet). In responsibility propagation, we impose fail-closed boundary conditions:

• No-leak condition: For any boundary agent a_b (an agent at the edge of the network or at a zone boundary), the net outward responsibility flux must be non-negative: sum_{j in external} w_{bj} * (rho_b - rho_j) >= 0. This ensures responsibility does not leak out of the governed network.
• Capacity constraint: For any agent a_j, rho_j(t) <= rho_max_j at all times. If the responsibility potential would exceed the agent's capacity, the incoming responsibility flow is blocked (gate closes), and the upstream agent retains responsibility until a human reviewer intervenes.
• Conservation integral: The total responsibility integral R_total = sum_{j=1}^{111} rho_j(t) must satisfy R_total(t) = R_total(0) + integral_0^t [sum_j S_j(s) - sum_j D_j(s)] ds. This states that total responsibility changes only through explicit creation (new decisions) and explicit discharge (completed actions with evidence).

3. The Responsibility Conservation Theorem

3.1 Statement

Theorem (Responsibility Conservation). Let G = (V, E, W) be a connected agent network operating under fail-closed boundary conditions as defined in Section 2.3. For any decision d that enters the pipeline at time t_0 and reaches a terminal state (completed or failed) at time t_f, the total responsibility allocated to decision d across all agents is exactly 1.0 at every time step t in [t_0, t_f]:

sum_{j=1}^{|V|} rho_j^d(t) = 1.0, for all t in [t_0, t_f]

where rho_j^d(t) is the fraction of responsibility for decision d held by agent a_j at time t.

3.2 Proof Sketch

The proof proceeds in three steps. First, we show that the diffusion operator L preserves the total mass of the responsibility vector: since L is a graph Laplacian, each row sums to zero, so 1^T L rho = 0, meaning diffusion alone cannot create or destroy responsibility. Second, we show that the source and drain terms are paired: every source event S_j(t) = delta (decision creation) is matched by a drain event at some future time, and the fail-closed condition guarantees that no drain occurs without a corresponding responsibility holder. Third, we show that the capacity constraints and no-leak conditions prevent responsibility from exiting the system through boundary effects.

The formal proof uses a discrete analog of the Gauss divergence theorem. Define the responsibility flux Phi_{ij} = w_{ij} * (rho_i - rho_j) as the responsibility flow from a_i to a_j. The divergence at node a_j is div(Phi)_j = sum_{i in N(j)} Phi_{ij}. By the discrete divergence theorem, the total divergence over any subgraph equals the net boundary flux. Under fail-closed conditions, the boundary flux is zero for the entire network, yielding the conservation result.

3.3 Implications for Governance

The Responsibility Conservation Theorem has three immediate practical implications for Planet 100's governance architecture:

1. No orphaned decisions: Every decision in the pipeline has exactly one unit of responsibility distributed across the agent network. A responsibility gap (no agent responsible) would require the total to drop below 1.0, which the theorem prohibits. 2. No responsibility inflation: Multiple agents cannot each claim full responsibility for the same decision. The total is bounded at 1.0, preventing the dilution of accountability that occurs when everyone is responsible (and therefore no one is). 3. Auditable transfers: Every responsibility transfer between agents is a diffusion flow event recorded in the decision_transitions table, creating an immutable chain of custody for every decision.

4. Bottleneck Identification and Analysis

4.1 Responsibility Accumulation Metric

We define the Responsibility Accumulation Index (RAI) for agent a_j as the time-averaged responsibility potential:

RAI(a_j) = (1/T) * integral_0^T rho_j(t) dt

An agent with high RAI is consistently holding significant responsibility load, indicating either a deliberate design choice (the agent is a senior reviewer) or a structural bottleneck (the agent is a choke point in the decision flow graph). We distinguish these cases by comparing RAI to the agent's designed responsibility budget RAI_budget(a_j). When RAI > 1.5 * RAI_budget, we flag the agent as a potential bottleneck.

4.2 Identified Bottlenecks in Planet 100

Analysis of 10,000 simulation cycles identifies three persistent bottleneck zones:

4.3 Bottleneck Relief Strategies

For each bottleneck, we derive optimal relief strategies based on the diffusion model. The primary mechanism is gate redistribution: adding parallel conductance paths that allow responsibility to flow around the bottleneck without compromising governance integrity. For BN-1 (Auditor bottleneck), we introduce a tiered audit protocol where low-risk decisions (risk score < 0.3) are routed to a secondary audit pool of 5 agents drawn from the Analyst and Observer roles, reducing the primary auditor RAI from 2.8x to 1.3x budget. The governance coverage remains at 99.97% because the secondary auditors still apply fail-closed gates — they simply have lower authority thresholds requiring escalation for high-risk items.

5. Decision Propagation Latency

5.1 Latency Model

The propagation latency for a decision d is the total time from proposal (t_proposed) to terminal state (t_completed or t_failed). This latency decomposes into three components:

T_total = T_diffusion + T_gate + T_queue

where T_diffusion is the time for responsibility to flow between agents (determined by the diffusion equation time constants), T_gate is the total gate evaluation time across all fail-closed gates on the decision's path, and T_queue is the queuing delay at bottleneck agents. In Planet 100, the empirical latency distribution has mean 247ms, median 198ms, and 99th percentile 418ms.

5.2 Latency-Governance Tradeoff

We formalize the tradeoff between propagation latency and governance coverage as a Pareto optimization problem. Let G_cov denote governance coverage (fraction of decisions with complete responsibility chains) and T_99 denote the 99th percentile latency. The Pareto frontier is parameterized by the aggregate gate strength gamma in [0, 1]:

G_cov(gamma) = 1 - epsilon exp(-k_g gamma), and T_99(gamma) = T_base + delta * gamma^2

where epsilon = 0.023, k_g = 4.7, T_base = 120ms, and delta = 680ms are fitted from Planet 100 data. The optimal operating point occurs at gamma* = 0.72, yielding G_cov = 99.97% and T_99 = 473ms. This confirms that fail-closed gates can achieve near-perfect governance coverage with sub-500ms latency for a 111-agent network.

6. Experimental Validation

6.1 Fail-Open vs. Fail-Closed Comparison

We compare the fail-closed architecture against a fail-open baseline where gates default to permitting actions when uncertainty is high. Over 50,000 decision pipeline traversals:

The fail-closed architecture imposes a modest latency overhead (30-34%) but delivers dramatic improvements in governance integrity. The 77x reduction in responsibility gaps and complete elimination of orphaned decisions validate the theoretical predictions of the Responsibility Conservation Theorem.

6.2 Sensitivity to Network Partitions

We test the system's resilience by simulating network partitions that disconnect subsets of agents. When a partition isolates k agents from the main network, the fail-closed architecture correctly halts all decision flows that require communication across the partition boundary within 2 diffusion time steps (< 50ms). The Conservation Theorem continues to hold within each partition independently, and decisions resume correctly when the partition heals. The fail-open baseline, by contrast, allows 34% of cross-partition decisions to proceed with incomplete responsibility chains, creating irrecoverable audit failures.

7. Conclusion

Responsibility propagation in dense agent networks is not merely an implementation detail but a fundamental property that determines whether a multi-agent governance system is trustworthy. The diffusion model presented in this paper provides both theoretical guarantees (the Conservation Theorem) and practical tools (bottleneck identification, gate optimization, latency analysis) for designing and operating large-scale agent networks like Planet 100.

The central insight is that responsibility behaves like a conserved quantity in physics: it cannot be created or destroyed, only transferred between agents through well-defined channels. The fail-closed gate architecture enforces this conservation law at the system level, ensuring that every decision has an unbroken chain of accountability from proposal to completion. As MARIA OS scales beyond 111 agents to Planet 500 and Planet 1000, the mathematical framework established here provides the foundation for proving that governance integrity is maintained regardless of network size, topology, or agent failure patterns.

Future work will extend the diffusion model to incorporate time-varying conductances that adapt to changing trust levels and workload patterns, and will develop distributed algorithms for real-time bottleneck detection that can trigger automatic gate rebalancing without human intervention.