The standard pattern in AI-assisted development is a single model invocation: a developer writes a prompt, a model returns code, and the developer evaluates the result. This pattern is efficient for short, well-defined tasks — generating a utility function, completing a boilerplate block — but it becomes unreliable for complex, high-stakes outputs where correctness is non-negotiable. A single model has no mechanism for doubting its own output, no way to compare alternative approaches, and no structured process for catching the subtle errors that emerge from the interaction of multiple design decisions.

The Ludopoly consensus mechanism addresses this limitation by requiring that every production artefact be independently produced by multiple specialised agents and then evaluated through a multi-round scoring protocol. The mechanism ensures that no single model's biases, hallucinations, or blind spots dominate the final output.

In the first round, each participating agent produces a candidate artefact — a contract implementation, a test suite, an optimisation proposal. These candidates are independent; no agent sees another's work during production. In the second round, the orchestration layer collects all candidates and presents them to a cross-evaluation stage, where agents score one another's work according to domain-specific criteria: correctness, gas efficiency, adherence to standards, test coverage, and documentation quality.

Each criterion carries a weight that varies with the task context. A DeFi staking contract assigns higher weight to security and reentrancy resistance. An ERC-20 token gives higher weight to gas efficiency and standard compliance. A governance module elevates access control and upgradeability considerations. These weights are not manually configured for every request — they are inferred from the project specification and the knowledge engine's understanding of domain priorities.

A static weighting system would impose the same evaluation criteria on every project, regardless of context. The Ludopoly consensus mechanism avoids this rigidity by dynamically adjusting each agent's influence based on the specific characteristics of the request. This adjustment is informed by three signals: the domain classification of the project (DeFi, NFT, gaming, governance), the risk profile of the target chain (mainnet versus testnet, high-value versus experimental), and the historical performance of each agent on similar tasks.

The result is a system that amplifies the voice of the most relevant expert for each task. A gas optimisation agent that consistently performs well on ERC-1155 batch operations will carry more influence when the next ERC-1155 project arrives. A security agent with a strong track record on proxy-pattern contracts will receive elevated weight when an upgradeable contract is being produced. This adaptive reweighting ensures that the consensus improves over time as the platform accumulates production history.

Not every output meets the quality threshold on the first attempt. When the composite score falls below the required level, the platform enters a self-correction cycle. The agents that contributed the lowest-scoring components receive targeted feedback — the specific criteria they failed, the evaluation context, and, where applicable, the alternative approaches that scored higher. They then revise their output and resubmit.

If the self-correction cycle does not converge after a configured number of iterations, the platform escalates the request. Escalation can take several forms: splitting the task into smaller sub-tasks, requesting additional context from the developer, or engaging a higher-capability model that was not part of the initial agent configuration. This graduated escalation ensures that difficult tasks are not abandoned but are instead addressed with progressively more resources.