Agent Capsules: Quality-Gated Granularity Control for Multi-Agent LLM Pipelines

2026-06-26 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Software Development & Engineering · Depth: Expert, extended

Summary

Agent Capsules is an adaptive execution runtime designed to optimize multi-agent LLM pipelines by reducing token costs while maintaining output quality. It addresses the "compound execution problem," where merging multiple agent calls into fewer LLM calls can save tokens but often degrades quality due to tool loss and prompt compression. The system employs a composition score, a quality gate, and an escalation ladder (standard, two-phase, sequential modes) to dynamically select the optimal execution strategy. A key finding is that quality recovery requires moving towards per-agent dispatch rather than enriching merged prompts. Benchmarks show Agent Capsules uses 51% fewer fine-mode and 42% fewer compound-mode input tokens than a hand-tuned 14-agent LangGraph pipeline, with quality gains of +0.020 and +0.017. It also achieves 19% fewer tokens than uncompiled DSPy at quality parity and 68% fewer than MIPROv2 at +0.052 quality on a 5-agent pipeline. The framework ensures efficiency and quality without per-model configuration or training data.

Key takeaway

For MLOps Engineers managing multi-agent LLM deployments, Agent Capsules offers a robust solution to optimize inference costs without sacrificing output quality. You should consider integrating this adaptive runtime to automatically manage execution modes, leveraging its quality gates and escalation ladder to ensure performance floors are met. This approach can significantly reduce input tokens by 42-51% and output tokens by 63-64% on models like Sonnet and Haiku, providing substantial cost savings and improved latency compared to hand-tuned or compile-time baselines.

Key insights

Agent Capsules dynamically optimizes multi-agent LLM pipelines for token efficiency and quality using an adaptive execution runtime.

Principles

• Merging agents saves tokens but risks quality.
• Quality recovery needs un-merging, not richer merged prompts.
• Adaptive runtime can match oracle routing without configuration.

Method

The runtime computes a composition score, applies a quality gate, and uses an escalation ladder (standard, two-phase, sequential) to adaptively select execution modes per group.

In practice

• Configure "quality_floor" per customer tier.
• Use "RedisBackend" for shared controller state.
• Avoid compounding single-agent groups.

Topics

• Multi-Agent Systems
• LLM Inference Optimization
• Adaptive Controllers
• Token Efficiency
• Compound Execution
• Quality Gates

Best for: AI Architect, AI Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.