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Posts tagged with GAIN

Implementing Agent Networks: GAINs and HCIN on Real Agents (Claude Code, Codex, OpenClaw, Hermes)

We introduced GAINs and HCINs as the architecture of multi-agent AI. The obvious question now is: how do you actually build one? The striking answer is that the agent tools developers already use — Claude Code, OpenAI Codex, OpenClaw, and Hermes — have each, independently, implemented the GAIN/HCIN primitives. Their defaults even encode the same lessons the architecture points to. GAINs and HCIN are the map; these tools are the implementation surface.

The mapping

Stripped to primitives, the correspondence is close to one-to-one:

  • Coordinator (CCA / PAC) → Claude Code's team lead; Codex's orchestrator (or an Agents-SDK
Implementing Agent Networks: GAINs and HCIN on Real Agents (Claude Code, Codex, OpenClaw, Hermes) Read more

Multi-Agentic Workflow Design using GAINs and HCINs

Explore our innovative framework Designed for robustness, scalability, and intelligent multi-agent collaboration, it enhances usability and operational efficiency in deploying language models.

Multi-Agentic Workflow Design using GAINs and HCINs

GAINs and HCINs give you two shapes for a multi-agent workflow: the flat network and the tiered one. This is a practical guide to designing with them, updated against the contemporary, verified evidence — including the most important rule, which is knowing when not to build a network at all.

Step 0 — Should this be a network?

Start here, because the seductive failure is reaching for agents because it sounds powerful. A network multiplies token cost by roughly an order of magnitude and adds a failure surface at every seam; the largest study of multi-agent failures (MAST) found high

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Agentic Workflows: The Power of AI Agent Collaboration Featured Post

Discover the potential of Agentic Workflows, an innovative approach to AI collaboration that leverages specialized agents, advanced prompt engineering, and iterative processes to tackle complex problems and drive technological innovation.

Agentic Workflows: The Power of AI Agent Collaboration

"Agentic Workflow" might seem like a novel term that's recently entered the lexicon of technology and artificial intelligence enthusiasts. However, the concept itself isn't exactly new.

Over the past year, we've been having burgeoning conversation around this idea of AI Agents, hinting at its emerging significance in the realm of AI and how we interact with these advanced systems.

But what does "Agentic Workflow" truly entail? It's time to look deeper into this term, exploring its nuances, origins, and implications in the context of our ever-evolving digital landscape.

Let's unravel the layers of "Agentic

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Distinguishing Between Chains, Agents and Generative AI Networks

This article explores Generative AI Networks (GAINs) - chains of interconnected AI agents that collectively solve complex problems with scalability, expertise, and resilience.

Distinguishing Between Chains, Agents and Generative AI Networks

In the field of Generative Artificial Intelligence, understanding the distinction between chains and agents is crucial for grasping how AI systems function and are implemented in various real-world applications.

The Functionality and Application of Chains in AI

Chains in AI refer to sequences of tasks or operations that are executed in a specific order. They are fundamental to the structuring of AI processes, offering a systematic approach to handling complex tasks. Let’s explore their key functionalities and applications:

Getting Started with Prompt Chaining
Master prompt chaining to accomplish virtually any task by transforming complex goals into seamless workflows.
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Hierarchical Collective Intelligence Networks (HCIN)

Beyond the limits of solitary intelligence, a new frontier is emerging in AI - one powered not by individual models, but by expansive collectives of specialized agents working together in symbiotic coordination. Welcome to the dawn of emergent cognition.

Hierarchical Collective Intelligence Networks (HCIN)

Hierarchical Collective Intelligence Networks (HCIN) are the tiered evolution of GAINs: what you build when one coordinator over a flat pool of specialists stops scaling. The network grows tiers, controls who can see what, nests sub-networks inside agents, and runs on a shared memory substrate. This is a refresh of HCIN against the contemporary, verified evidence — and HCIN's most distinctive idea turned out to anticipate one of the genuinely load-bearing findings in multi-agent research.

From flat GAIN to tiered HCIN

A single coordinator becomes a bottleneck once the agent pool is large or the task is

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Fragmented Intelligence: The New Face of AGI Featured Post

The myth of a singular, omnipotent artificial general intelligence is dead. The future lies in a mosaic of ephemeral, specialized AI agents, working in concert under human direction. A decentralized network, not a monolith. This new paradigm promises to reshape the pursuit of AGI.

Fragmented Intelligence: The New Face of AGI

The journey towards artificial general intelligence (AGI) has historically been conceived as a quest to create a monolithic, all-encompassing intelligence. However, we have stated two major things at PromptEngineering.org that we see coming to pass since yesterday's announcement by OpenAI—one where AGI is achieved through a multitude of specialized, ephemeral AI agents working in concert through GAINs (Generative AI Networks). This shift from a singular entity to a legion of specialized agents could redefine the pursuit of AGI, leading to a mosaic of intelligence rather than a single, unified consciousness.

What Are Large Language Model (LLM)
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Reasoners - A New Approach to Smarter AI

Reasoners “thinking” before responding, improving logic and problem-solving without larger models. They excel in structured tasks but struggle with creativity. A $30 experiment showed this approach could make AI smaller, cheaper, and more efficient, reshaping the future of AI development.

Generative AI - The New Compiler

There’s been a lot of noise lately about AI replacing programmers. Apps like Cursor, Windsurf, Loveable, Cline, Aider, Bolt, and others have sparked heated debates, often painted in stark black-and-white terms: either AI will replace programmers, or it won’t. But that framing misses the point. The

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