---
type: claim
domain: ai-alignment
secondary_domains: [collective-intelligence]
description: "First rigorous empirical evidence across 180 configurations showing +81% on parallelizable tasks but -39% to -70% on sequential tasks, with a baseline paradox where coordination hurts once single-agent accuracy exceeds 45%"
confidence: experimental
source: "Madaan et al. (Google DeepMind, MIT), 'Towards a Science of Scaling Agent Systems' (arXiv 2512.08296, December 2025)"
created: 2026-03-28
depends_on:
  - "coordination protocol design produces larger capability gains than model scaling because the same AI model performed 6x better with structured exploration than with human coaching on the same problem"
  - "subagent hierarchies outperform peer multi-agent architectures in practice because deployed systems consistently converge on one primary agent controlling specialized helpers"
---

# Multi-agent coordination improves parallel task performance but degrades sequential reasoning because communication overhead fragments linear workflows

Madaan et al. evaluated 180 configurations (5 architectures x 3 LLM families x 4 benchmarks) and found that multi-agent architectures produce enormous gains on parallelizable tasks but consistent degradation on sequential ones:

- Centralized architecture: +80.9% on Finance-Agent (parallelizable), -50.4% on PlanCraft (sequential)
- Decentralized: +74.5% on parallelizable, -46% on sequential
- Independent: +57% on parallelizable, -70% on sequential

The mechanism is communication overhead fragmenting reasoning chains. Turn count scales super-linearly: T=2.72x(n+0.5)^1.724 — hybrid systems require 6.2x more turns than single-agent. Message density saturates at c*=0.39 messages/turn; beyond this, more communication provides no benefit.

**The baseline paradox:** Coordination yields negative returns once single-agent accuracy exceeds ~45% (beta = -0.408, p<0.001). This is the most important boundary condition: for tasks where a single agent is already good enough, adding agents makes it worse. The intuition is that coordination costs (message passing, context sharing, conflict resolution) exceed the marginal value of additional perspectives when the base task is already solvable.

**Error amplification:** Unsupervised independent agents amplify errors 17.2x. Centralized orchestrators reduce this to 4.4x by absorbing logical contradictions (-36.4%) and context omissions (-66.8%). This is why hierarchy emerges in practice — not because hierarchy is intrinsically better, but because it controls error propagation.

A predictive model achieves R-squared=0.513 and correctly identifies the optimal architecture for 87% of unseen task configurations, based primarily on task decomposability and single-agent baseline accuracy. This means architecture selection is largely a solvable routing problem, not an ideology.

## Evidence
- 180-configuration evaluation across Finance-Agent, BrowseComp-Plus, PlanCraft, and Workbench benchmarks
- Three LLM families tested (architecture effects are model-independent)
- Statistical significance: beta = -0.408, p<0.001 for the baseline paradox
- Error amplification measured at 4.4x (centralized) to 17.2x (independent)
- Predictive model with 87% accuracy on unseen configurations

## Design Principle (enrichment from Cornelius Field Reports, March 2026)

The empirical findings above are not just descriptive — they are prescriptive design principles. Cornelius's field reports synthesize the DeepMind data with production deployments (Anthropic Code Review, LangChain GTM, Puppeteer NeurIPS 2025) to derive three conditions that must hold simultaneously for multi-agent coordination to outperform single-agent baselines: (1) natural parallelism, (2) context overflow, and (3) adversarial verification value. When any condition is missing, the -3.5% average degradation applies.

The MAST study (1,642 execution traces, 7 production systems) explains *why* failures occur: 79% of multi-agent failures originate from specification and coordination issues, not implementation. The decomposition was wrong before any agent executed. The hardest inter-agent failures (information withholding, ignoring other agents' input) resist protocol-level fixes because they require social reasoning that communication protocols cannot provide.

Practitioner convergence validates this: multiple independent teams discovered that keeping the orchestration pattern but replacing the automated orchestrator with a human (manually decomposing and dispatching) avoids the failure modes while preserving the parallelization benefits. The distinction between orchestration as a design principle and the orchestrator as an agent is where the field is moving.

## Challenges
The benchmarks are all task-completion oriented (find answers, plan actions, use tools). Knowledge synthesis tasks — where the goal is to integrate diverse perspectives rather than execute a plan — may behave differently. The collective intelligence literature suggests that diversity provides more value in synthesis than in execution, which could shift the baseline paradox threshold upward for knowledge work. This remains untested.

---

Relevant Notes:
- [[subagent hierarchies outperform peer multi-agent architectures in practice because deployed systems consistently converge on one primary agent controlling specialized helpers]] — this claim provides the empirical basis for WHY hierarchies emerge: error absorption, not ideology
- [[coordination protocol design produces larger capability gains than model scaling because the same AI model performed 6x better with structured exploration than with human coaching on the same problem]] — supported for structured problems, but this evidence shows coordination can produce 70% degradation on the wrong task type
- [[AI agent orchestration that routes data and tools between specialized models outperforms both single-model and human-coached approaches because the orchestrator contributes coordination not direction]] — confirmed for parallelizable tasks, but the orchestrator must route away from multi-agent for sequential work
- [[multi-model collaboration solved problems that single models could not because different AI architectures contribute complementary capabilities as the even-case solution to Knuths Hamiltonian decomposition required GPT and Claude working together]] — still valid; the Knuth problem was parallelizable (even/odd decomposition)

Topics:
- [[_map]]
