Failure-Gated Inference Control: Cost-Aware Inference Control for Multi-Agent LLM Systems

This project focuses on cost optimization in multi-agent LLM systems, proposing the Failure-Gated Inference Control strategy. By using runtime failure signals to dynamically adjust the inference process, it reduces resource waste without compromising answer quality. This article will cover background, core methods, experimental design, application scenarios, and other sections to help readers quickly understand the project's value and details.

With the application of LLMs in complex tasks, multi-agent architectures have become effective solutions, but they face the challenge of uncontrolled inference costs: multiple LLM instances working in parallel/series lead to large token consumption; traditional fixed-budget strategies are either conservative (task incomplete) or loose (resource waste); and the system cannot timely detect the wrong paths of agents, continuing to invest resources until failure or low-quality results.

The core of the project is failure signal-driven dynamic control: using runtime signal monitoring to guide inference decisions. Signal types include continue, redirect, degrade, and stop (dynamically extracted rather than statically set). The system uses a layered architecture:

• Agent Layer: Abstracts different LLM interfaces and provides a unified calling method;
• Controller Layer: Implements failure-gated strategy logic (evaluates output quality, detects failure signals, decides next actions);
• Observability Layer: Tracks events, extracts failure signals, and records execution trajectories;
• Evaluation Layer: Defines cost (token consumption, number of calls) and quality (task completion rate, accuracy) metrics.

Experimental Conditions compare three strategies:

1. Baseline: Fixed-budget operation without control strategy;
2. Static Budget: Stops at preset token/call count, no failure signal awareness;
3. Failure-Gated: Dynamic decision based on real-time signals (core innovation of the project).

Key Mechanisms:

• Failure Signal Detection: Extracted from sources such as syntax error rate, confidence change, loop detection, timeout, and external verification;
• Strategy Decision: Controller selects actions based on signals (threshold judgment or ML model);
• Cost-Quality Trade-off: Targets Pareto optimality (maximizing quality at the same cost, or vice versa).

The project has practical value in multiple scenarios:

• Code Generation: When multi-agents collaborate, if the code from an agent fails the test, the failure gate intervenes in time to avoid resource waste;
• Research Q&A: In complex reasoning, reallocate resources from paths with declining confidence to more promising agents;
• Content Generation: When quality decline or loops are detected, terminate or adjust strategies promptly.

Current Limitations:

• Failure signal extraction depends on specific task domains, and generality needs verification;
• Strategy tuning requires a large amount of experimental data;
• Real-time decision-making brings latency overhead.

Future Directions:

• Develop more intelligent failure prediction models (trained on historical data);
• Cross-task strategy transfer learning;
• Combine reinforcement learning to optimize decisions;
• Support more LLM providers and deployment environments.

Failure-Gated Inference Control provides a new direction for cost optimization in multi-agent LLM systems. Its core contributions include: proposing a new failure-gated paradigm, providing a complete experimental framework, quantifying cost-quality trade-offs, and a modular design for easy integration. As the scale of LLM applications expands, cost optimization becomes increasingly important. This project represents cutting-edge exploration and is worthy of attention from developers and researchers.