The Good Tech Companies - How Multi-Agent Systems Are Rethinking LLM Architecture

Episode Date: March 19, 2025

This story was originally published on HackerNoon at: https://hackernoon.com/how-multi-agent-systems-are-rethinking-llm-architecture. Multi-agent AI systems are redefining LLMs, improving accuracy, transparency, and real-world applications. Discover how Skillfully is leading this evolution. Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #multi-agent-ai-systems, #llm-architecture-innovation, #ai-driven-hiring-solutions, #ai-transparency, #distributed-cognition-in-ai, #skill-based-ai-assessments, #collaborative-ai-models, #good-company, and more. This story was written by: @jonstojanjournalist. Learn more about this writer by checking @jonstojanjournalist's about page, and for more stories, please visit hackernoon.com. Large Language Models (LLMs) are capable of incredible feats in terms of natural language processing. But they frequently stumble when faced with complex, multi-step reasoning tasks where accuracy and transparency are non-negotiable. This requires an essential reimagining of how LLMs work, says James Kanjirathinkal.

This audio is presented by Hacker Noon, where anyone can learn anything about any technology. How multi-agent systems are rethinking LLM architecture, by John Stoyan Journalist. With the rapid rate at which artificial intelligence, i, is evolving, turning points in its development often feel as if they happen all the time. Today's large language models, LLMs, have grown to be capable of incredible feats in terms of natural language models, LLMs, have grown to be capable of incredible feats in terms of natural language processing, but they frequently stumble when faced with complex, multi-step reasoning tasks where accuracy and transparency are non-negotiable. Their tendency toward unpredictable hallucinations and inconsistent ability to handle complex,

multi-step reasoning have made these single-agent AI systems unreliable in scenarios where accuracy, transparency, and adaptability are essential. Some models are better than others for any given task, but picking the best tomeat your goals can sometimes feel like rolling dice. Many experts are tired of fine-tuning the output of their results, but this requires an essential reimagining of how LLMs work. Johnson James Kanjirathinkle, co-founder and VP of product at Skillfully, a recruitment and onboarding platform replacing outdated credential based hiring with iDriven, skill focused assessments, questions why LLMs run on a

single model system in the first place. The new concept of multi-agent AI systems is already in place at Skillfully, but this is only one angle on the potential for multi-agent AI to regrow LLMs for all kinds of real-world applications. This approach focuses on distributed cognition through multi-agent AI systems that mirror how human teams function in professional settings. The limitation of single-agent LLMs in production, as versatile as they can be, single-model LLMs are fundamentally constrained by their design architecture when applied to high-stakes business decisions. Despite sophisticated

parameter counts and extensive training data, they function as unified systems with several inherent technical limitations in ability to effectively cross-verify their own outputs. Lack of specialized focus on different cognitive subtasks. Limited ability to maintain consistency across complex reasoning chains. Insufficient transparency in how conclusions are reached. For example, any business wanting to keep pace in today's market will require i.That can assess candidates' capabilities fairly and accurately. Human hiring experts rely on a mix of data and well-honed professional intuition, while traditional AI systems asked to do the same will tend to use past job titles or educational credentials as proxies for ability rather than evaluate actual skills.

As a result, AI can generate inaccurate or misleading outcomes when asked to make decisions based on indirect signals rather than concrete performance data. Perhaps more importantly, the lack of robust cross-verification mechanisms led to results lacking explainability and clear assessment frameworks, leaving hiring managers with inscrutable recommendations when what they need most ispractical guidance. For AI to be truly useful in business, it must provide actionable, transparent decision making rather than just surface-level predictions. The multi-agentic solution to the problem of LLM

nuance. When he was confronted with this same problem while developing skillfully, Kanjira Thinkle focused on creating an LLM architecture that thinks differently in one key foundational manner. Rather than relying on a single model to handle everything, multi-agentic workflows distribute tasks among specialized AI agents instead. These agents work together, each focusing on a distinct function such as contextual understanding, task execution, or quality control to produce more accurate and reliable results. This new way of thinking is already live on Skillfully's hiring platform, which uses

orchestrated multi-agent AI systems to provide a more reliable, human-centric hiring process. Skillfully's AI agents assess skills dynamically, analyzing how individuals perform in simulated environments. By having multiple AI agents cross-verify insights, the system significantly reduces the risk of hallucinations and erroneous recommendations, providing a clearer, more accurate picture of candidate capabilities. Kanjira Thinkle also worked to make explainability a cornerstone of the system. Skillfully's hiring recommendations are backed by predefined assessment rubrics, transparent scoring mechanisms, and detailed rationales provided by the LLM's multiple agents.

Just like a human recruitment expert, Skillfully's recommendations are defensible and nuanced. Unlike human experts, these recommendations come with robust bias mitigation through multi-agent cross-checks built on consistent evaluation frameworks that create clear audit trails. This next-level transparency is also getting ahead of emerging AI regulations, which might catchless nuanced LLMs unprepared. Technical challenges and future developments. Despite its advantages, implementing such as the unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, unshared, uns computational requirements. These challenges represent the next frontier for AI architecture. As Kanjira Thinkle has demonstrated at Skillfully, the benefits of distributed cognition outweigh

these implementation hurdles when the stakes involve human outcomes like fair hiring practices. Multi-Agentic Workflows for a Less Monolithic Future Johnson James Kanjira Thinkle is adamant that the AI systems of tomorrow will be specialized to complement human capabilities in real-world applications, with the success of multi-agentic workflows in hiring a critical test case for new modular, collaborative AI systems that can succeed across multiple spheres. He believes that AI must move beyond monolithic, single-agent models in order to tackle real-world problems effectively.

Multi-agentic workflows offer a more reliable, scalable, and interpretable alternative that aligns AI with human needs rather than forcing businesses to adapt to AI's limitations. Multi-agent systems reimagine AI less as a single operator and more as a team of specialists who can challenge, refine, and build on each other's insights. Kanjira Thinkle has demonstrated one use for this in Skillfully, turning what was once a rigid, opaque process into something more flexible and fair. That same shift is possible anywhere AI is asked to make decisions that matter. Thank you for listening to this Hacker Noon story, read by Artificial Intelligence.

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