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1 change: 1 addition & 0 deletions src/layouts/BaseLayout.astro
Original file line number Diff line number Diff line change
Expand Up @@ -20,6 +20,7 @@ const { title } = Astro.props;
<ul>
<li><a href="/">Home</a></li>
<li><a href="/projects">Projects</a></li>
<li><a href="/research">Research</a></li>
<li><a href="/contact">Contact</a></li>
</ul>
</nav>
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81 changes: 81 additions & 0 deletions src/pages/research/area.astro
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---
export const prerender = true;

import BaseLayout from '../../layouts/BaseLayout.astro';
---

<BaseLayout title="AREA — Blind Tech Mage">
<main id="main-content">
<h1>AREA</h1>
<p>Agentic Reasoning and Engagement Analysis</p>

<p>
AREA is the first phase of Tech-Clusive Solutions&rsquo; multi-agent
systems research program &mdash; a study of how AI agents actually
behave when several of them have to coordinate, rather than how they
perform in isolation. I run it as Principal Investigator on behalf of
Tech-Clusive Solutions LLC.
</p>

<h2>The question</h2>
<p>
No existing research into agentic systems clearly defines how the
individual agents in a multi-agent deployment behave as specific
complexity factors vary across a session. AREA sets out to measure
that behavior with a reasonable degree of confidence, organized
around three foundational research questions:
</p>
<ol>
<li>
Given specific criteria, how confidently can we predict how
multi-agent AI systems will behave in concert with one another?
</li>
<li>
Given specific criteria, how confidently can we predict how
multi-agent AI systems behave in concert with humans?
</li>
<li>
How closely do functional cognitive models map from human-based
systems to mixed human and AI systems?
</li>
</ol>

<h2>The hypothesis</h2>
<p>
Given specific contextual parameters &mdash; deployment environment,
agent configuration, and task type &mdash; the degree of structural
integrity and richness in the instructions given to a multi-agent AI
system is positively correlated with the collective coordination
quality of the group, independent of any individual agent&rsquo;s own
task completion rate.
</p>

<h2>How it&rsquo;s studied</h2>
<p>
AREA runs controlled scenarios in which agents are given tasks built
from a sequence of discrete actions, with instruction coherency,
completeness, complexity, and context systematically varied from run
to run. Every action an agent takes &mdash; what it was told, what it
inferred, what it communicated to other agents, and why &mdash; is
recorded, so the resulting behavior can be traced back to the
specific conditions that produced it, not just scored as pass or
fail.
</p>

<h2>Where it fits</h2>
<p>
AREA is the first of three phases in Tech-Clusive Solutions&rsquo;
research arc: AREA studies what multi-agent systems <em>do</em>; the
next phase, CAIRE, studies what agents <em>are</em> &mdash; whether
cognitive processing profiles can be reliably assigned and mutually
modeled between agents; and the convergence phase, FROST, asks
whether a system can turn those findings on itself, safely.
</p>

<p>
<a href="https://github.com/TechClusiveSolutions/agentic-reasoning-and-engagement-analysis" target="_blank" rel="noopener noreferrer">View the program documentation on GitHub<span class="visually-hidden"> (opens in a new tab)</span></a>
</p>

<p><a href="/research">Back to research</a></p>
</main>
</BaseLayout>
26 changes: 26 additions & 0 deletions src/pages/research/index.astro
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---
export const prerender = true;

import BaseLayout from '../../layouts/BaseLayout.astro';
---

<BaseLayout title="Research — Blind Tech Mage">
<main id="main-content">
<h1>Research</h1>

<p>
The AI I use every day already describes an interface to me. The
research I&rsquo;m doing now asks what happens when AI agents stop
just describing, and start acting &mdash; and coordinating with each
other while they do it.
</p>

<ul>
<li>
<a href="/research/area">AREA</a> &mdash; Agentic Reasoning and
Engagement Analysis. A study of how multi-agent AI systems
coordinate, and what makes that coordination succeed or fail.
</li>
</ul>
</main>
</BaseLayout>
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