A Multi-agent Approach to Technology Intelligence: From Patents to Market Signals with Large Language Models

Abstract

Technology Intelligence (TI) draws on knowledge management and competitive intelligence to give decision-makers awareness of technological trends, opportunities and threats. Recent frontier large language models and autonomous agents make it feasible to automate the full TI lifecycle. This paper presents TechIntel-MAS, an eight-role multi-agent system implemented on the CrewAI framework, that transforms a body of disclosed patent filings, supplied alongside a free-text intelligence request, into a structured competitive-intelligence report. The system treats patents as seed signals and searches the open web for corresponding market evidence (products, vendors, deployments, partnerships), and reports the absence of evidence honestly when none is found. Six frontier models (GPT-5.4, Claude Sonnet 4.6, Gemini 3.1 Pro, DeepSeek R1, Moonshot Kimi K2 Thinking, Qwen3 Max Thinking) execute the generative roles in parallel. An independent evaluator pool (Grok 4 and Llama 4 Maverick) operates the Verifier, Telemetry and Judge roles, preserving the principle that the model evaluating an artefact is never the one that produced it. We evaluate the pipeline on four heterogeneous patent filings and report per-model precision, recall, F1, semantic alignment, KIQ-relevancy and a coarse expected calibration error, together with cost and elapsed-time figures from a fully instrumented run. We make three contributions: a TI-specific eight-role architecture aligned with the Kerr three-tier model; a patent-as-seed-signal retrieval pattern that grounds disclosed inventions in open-web market evidence; and an evaluation methodology with strict generator-evaluator independence, evaluated across six frontier LLMs in a pilot study. The paper situates these results within the knowledge-management literature and discusses implications for organisational learning and strategic forecasting in agentic settings.