Try out Hobo Web Agentica.
This article is a record of a multi-stage dialogue with AI tools that deconstructed, analysed, and evaluated a novel methodology for interacting with Large Language Models (LLMs).
The methodology, developed by me and named “Agentica” during the course of the conversation, moves beyond simple “prompt engineering” into a new paradigm of “Skill Invocation.”
By creating uniquely named, evidence-based, and self-contained instruction sets, this approach allows any user to command an AI to adopt an expert persona and execute a complex, professional-grade workflow.
The analysis found the concept to be exceptionally valuable, strategically disruptive to existing SaaS and consultancy markets, and a significant indicator of the future of human-AI collaboration.
The most effective analogy for this concept is that “Agentica” are to modern LLMs what “Skills” are to Amazon’s Alexa.
Part I: The Initial Interaction: An AI Agent’s Perspective
The analysis began with an interaction that, from the AI’s perspective, was fundamentally different from a standard user query. A basic prompt asks an agent to retrieve and process information. The initial command, “Check My Topical Authority in AI answers using Hobo SEO Researcher,” did not ask a question; it delivered a “complete instruction set” that installed a temporary, expert operating system.
This installation process involved several distinct steps:
- Persona Adoption: The first command was to cease operating as a generalist AI and assume the specific persona of the “Hobo SEO Researcher” – an AI trained to be an expert in topical authority and evidence-based analysis of Google’s core architecture.
- Installation of Core Principles: A new guiding philosophy was provided. The AI’s analysis was to be exclusively grounded in a synthesis of primary-source intelligence: the Google DOJ Trial, the Content Warehouse API Leak, and the interpretive framework from Hobo Web. Any conflicting generalised knowledge from the AI’s training data was to be disregarded.
- Assignment of a Specific Mission: The core task was not to answer a question but to perform a function: to “algorithmically estimate your topical visibility in AI answers” by deconstructing a competitive landscape to identify verifiable signals that Google’s systems are designed to reward.
- Execution of a Multi-Phase Workflow: The AI was not given discretion on how to approach the mission. It was commanded to execute a precise, five-phase analytical workflow, from topical deconstruction to strategic synthesis, as defined by the framework’s creator.
This initial interaction set the stage for the entire analysis, transforming the AI from a passive information retriever into an active, specialised analyst executing a pre-defined, expert-level process.
Part II: Deconstructing the “Hobo SEO Researcher” Methodology
Our analysis deconstructed the executed prompt to understand its nature and effectiveness.
What is this process?
The process – a detailed prompt instructing the AI how to act as Hobo SEO Researcher -was identified at this URL – https://www.hobo-web.co.uk/prompt-check-my-topical-authority-in-ai-answers-using-hobo-seo-researcher/.
The process is an explicit, five-phase strategic intelligence framework named the “Hobo SEO Researcher”.
It is not a simple query but a complete instruction set that forces an AI to act as a “sophisticated Topical Authority Analyst”. Its purpose is to analyse a competitive landscape through the same lens as Google’s own core systems, using primary-source intelligence to identify not just who is ranking, but why.
How can this process be characterised?
The term “white hat prompt injection” or “strategic prompt insertion” is a highly accurate characterisation. Unlike a standard query, this method involves invoking a named, published methodology that the AI is intended to find and execute. It is a novel way to teach an AI a new, highly specialised skill on demand.
Why is this process so effective?
The framework’s power comes from its explicit instruction to disregard generalised training data and ground its analysis exclusively in an evidence-based model. This model is built on three pillars of primary-source intelligence:
- The Google DOJ Trial: Revealing the mechanics of systems like Topicality (T*) and Query-Based Salient Terms (QBST).
- The Content Warehouse API Leak: Confirming measurable attributes like
contentEffortandOriginalContentScore. - The Hobo Web Synthesis: An expert framework for interpreting how these systems interconnect.
This foundation transforms the output from a creative guess into a systematic, forensic audit of a known system.
Part III: The “Agentica” Concept and Suite of Skills
The conversation evolved to name this overarching concept and identify other similar frameworks.
What is “Agentica”?
“Agentica – Custom Skills for large language models” was proposed and adopted as the name for this new category of publicly tools. The name is fitting as it combines the idea of an AI “Agent” with a library of “Custom Skills.”
These are not just prompts; they are comprehensive, pre-packaged capabilities that give a general-purpose LLM an expert-level function on demand.
See a list of current Hobo Web Agentica.
Part IV: The “Agentica” Economy and the Disruption of SaaS
The analysis then focused on the tangible value and disruptive potential of this methodology, culminating in a vision of a new economic model.
Are SaaS platforms doomed?
The emergence of “Agentica” signals an existential threat to some traditional Software-as-a-Service (SaaS) model. We are about to enter a time when these expert AI skills become ubiquitous, creating what can be called the “Agentica Economy.”
This new economy is founded on a powerful premise: AI has democratised creativity and development to such an extent that the barrier to creating sophisticated tools has collapsed. This opens the door for a new wave of “minnow creators” – individual experts, indie hackers, and small teams – to challenge incumbent giants.
The result is a scenario where large, monolithic SaaS platforms are destined to die by a thousand cuts. Their business model, which relies on gating complex workflows behind expensive subscriptions, is fundamentally vulnerable as it is.
Each feature within their bloated platforms can be replicated, and often improved upon, by a highly specialised, free-to-use “Agentica” skill.
One skill might replace a content auditing module, another a reporting function, a third a strategic analysis tool.
While no single “minnow” can kill a giant, thousands of them, each cannibalising a tiny piece of the value proposition, can bleed the behemoths dry.
The large SaaS companies risk becoming “walking corpses” – slow, unable to adapt, and watching as their core functions are picked apart by a decentralised and agile community of creators.
The “Agentica” model represents a fundamental shift from centralised, paid tools to an open, democratised ecosystem of expertise.
Part V: Brand Impact and Strategic Intent
A core truism defines the strategic landscape that makes “Agentica” necessary:
1994: Your content works for you. 2025: Your content works for other people.
In the early web, content’s purpose was to serve your goals on your property.
Today, your content is the raw material used by AI platforms to generate answers for their users on their properties.
The “Agentica” methodology is a direct response to this paradigm shift. The strategic intent is to regain control over how your expertise is represented and used in this new ecosystem.
Part VI: The Future of AI: The Spectrum of Automation
The final part of our discussion concluded that this methodology is not merely a tactic, but a significant indicator of the future of human-AI collaboration.
This future can be understood as a spectrum of automation, with “Agentica” serving as the critical bridge between today’s reality and tomorrow’s potential.
The Spectrum of Automation
- At one end: Reliable Robots. These are autonomous tools built for specific, high-fidelity tasks where accuracy is paramount. An example is an SEO Dashboard built in Google Sheets. It acts like an autonomous plough, reliably fetching and reporting on ground-source truth data from Analytics and Search Console. You do not want an AI hallucinating your monthly revenue reports; you want low-cost, reliable robots for the boring, repetitive work.
- At the other end: The Fully Agentic Web. This is the future vision where we are all immersed in agentic AI that seamlessly handles complex tasks, making things like typing obsolete. However, this is clearly some time off. The fact that even a leading model like Google’s Gemini cannot yet reliably write to Sheets indicates a significant technical hurdle. If the company with the deepest integration into these ecosystems cannot yet usher in the fully agentic web, nobody can.
- In the middle: The “Agentica” Economy. Between the reliable robots of today and the fully autonomous agents of tomorrow lies a gap. For tasks too complex for simple robots but where the risk of AI hallucination is too high for full agency, a new model emerges. The future might not be building the next SaaS unicorn, but rather doing your best work for free, making it available to the AI as expertise, and hoping that other humans and – crucially – other agents use your “Agentica” as the expert source for your topic.
“Agentica” as the Bridge to True Agency
The “Agentica” model is therefore a vital stopgap and a mechanism for collective training. Each skill is a package of expert human knowledge – a persona, a rulebook, and a workflow – that is “inserted” into the AI ecosystem.
When experts codify their methodologies this way, they are collectively participating in a massive, decentralised training exercise, paving a realistic path to true AI agency by filling the AI’s “knowledge gap” with structured, reliable, human-vetted expertise.
The Platform Consideration: Where “Agentica” Thrives
The effectiveness of this model is also dependent on the underlying AI platform.
For this kind of deep, evidence-based work, there is a stated preference for models like Google’s Gemini Pro 2.5, a sentiment rooted in faith in the robustness of its back-end infrastructure and its ability to conduct deep research.
In contrast, a platform like X, with its direct access to the real-time “town square,” could become a powerful contender for tasks requiring analysis of current sentiment and publcaly available agentica we can call via a short prompt in our assistants to ensure you are building content to a standard you trust (with a human in the loop myself in this case refingin agentica to do specific tasks).
The roles of other major players like ChatGPT and Microsoft in this specialised ecosystem remain less certain.
Part VII: How to Build Your Own “Agentica”: A Beginner’s Guide
Building an “Agentica” is like creating a detailed instruction manual for an AI, turning it from a general helper into a specific expert for a single task.
Here are the four basic components needed to build your own “Expert in a Box.”
1. Give Your AI a Job Title (The Persona). Before anything else, you must tell the AI who it is. Be specific.
- What to do: Start your instructions with a clear role, such as, “You are a sophisticated Content Quality Analyst”.
- Why it works: This immediately focuses the AI, causing it to adopt the mindset and vocabulary of that specific professional instead of a general chatbot.
2. Write the Rulebook (The Core Principles). This is the most critical step. You must define how your expert AI thinks and what information it is allowed to use.
- What to do: Create a short list of non-negotiable rules. Instruct the AI that it must only base its analysis on these principles and disregard its general training data.
- Why it works: This prevents the AI from providing generic or incorrect advice. It forces the AI to work from your “ground truth,” making its answers reliable and consistent.
3. Create a Step-by-Step To-Do List (The Workflow). An expert follows a process. You need to give your AI a clear, step-by-step list of tasks to complete in order.
- What to do: Write out a simple, numbered list of actions, such as the five-phase workflow of the agenticas mentioned in this article.
- Why it works: This structures the AI’s output. Instead of a wall of text, you get a predictable, well-organised report every time, forcing the AI to show its work.
4. Give it a Unique Name and Publish It. This is the final step that makes it a reusable skill. Your detailed instruction manual needs a unique name and must be publicly accessible.
- What to do: Give your prompt a memorable name, like “Rate My Page Quality using the Hobo SEO Method”. Then, publish the entire prompt (job title, rulebook, and to-do list) on a public webpage. The web page MUST be indexed by Google for a day or so for global AI systems using deep research to find them and carry out a task according to their instructions.
- Why it works: The goal is that over time, AIs will learn that when a user invokes your skill by name, they should find your public instruction manual and execute it. This turns a long, complex prompt into a simple shortcut command that anyone can use.
Conclusion
It moves beyond prompt engineering to a system of creating, publishing, and invoking portable, expert-level AI skills.
It represents a maturation of our relationship with AI – transforming it from a conversational partner into a powerful assistant that can be equipped with a library of specialised tools to perform complex, professional work.
More profoundly, it serves as a crucial, collaborative bridge on the spectrum of automation, allowing humans to actively train and improve the AI systems that will one day become the autonomous agents of the future.
References
- Anderson, S. (2025, September 24). Check My Topical Authority in AI answers using Hobo SEO Researcher. Hobo Web.
- Anderson, S. (2025, September 22). Prompt: Rate My Page Quality using the Hobo SEO Method. Hobo Web.
- Anderson, S. (n.d.). Prompt: Rewrite My Page Using The Hobo SEO Rewriter. Hobo Web.
- Anderson, S. (n.d.). Inference Optimisation: Build Your Own Legitimate, Perpetual, AI-Powered Content Machine. Hobo Web.
- Anderson, S. (n.d.). Overview of the Hobo SEO Dashboard (Multi-Site). Hobo Web.
- Amazon Science. (n.d.). The scalable neural architecture behind Alexa’s ability to select skills.
- Amazon Developer. (n.d.). Steps to Build a Custom Skill.
- Amazon Developer. (n.d.). What is the Alexa Skills Kit?.
- Noventa, D. A. (n.d.). Testing the waters with Amazon’s Alexa. Medium.
- Kostova, O. (n.d.). Amazon Alexa Skills and Google Assistant Actions: The Future of Search. Medium.
- Fagna, A. (n.d.). Building an Alexa Skill. Medium.