Why your AI gives vague answers, and how specific context architecture fixes it
TL;DR
Most AI tools underperform for one reason: the model is guessing. Not because it is weak, because it has been given broad, generalised context and asked to figure out what is relevant. Specific context architecture, developed by Jake Van Clef and built into Anaboo's agentic systems, solves this by routing each agent to exactly the files it needs for each task. The result is fewer tokens wasted, sharper outputs, and AI that behaves like it was built for your business specifically, because it was.
Why does your AI give vague, generic answers?
The model is not the problem. The context is. When you hand an AI a large document, or a pile of documents, and ask it to help with a specific task, it has to search through everything to find what is relevant. It fills the gaps with general training knowledge rather than your specific business knowledge.
This is how most AI tools work. It is also why most AI tools produce generic outputs. The bigger the context, the more guessing happens. The more guessing, the less accurate the result. You also pay for every token the model reads, whether it needed to or not.
What is specific context architecture?
Specific context architecture is the practice of structuring your AI system so each agent loads only the information it needs for its current task, no more, no less. Jake Van Clef developed the specific file structure that Anaboo uses to address this problem directly. The architecture is built around one principle: every agent gets exactly the context it needs for its specific task, and nothing else.
Each area of the business, finance, operations, writing, marketing, research, lives in its own workspace. Each workspace has a CONTEXT.md file that acts as a routing table. It tells the agent: if you are doing this task, load these files and skip everything else.
How does the context routing work in practice?
A finance agent creating an invoice loads pricing rules and the products-services file. It does not load marketing guidelines or technical documentation. A marketing agent writing a blog post loads brand voice and the style guide. It does not load inventory procedures or financial reports.
Every agent has a defined role, a defined set of tools, and a defined list of skills. When a task comes in, the agent reads its CONTEXT.md, loads the correct files for that specific task, does the work, and exits. No drift. No guessing. No token waste on irrelevant material.
Why does broad context hurt output quality and increase cost?
Tokens are the currency of AI interaction. Every word the model reads and produces costs tokens. When an agent loads a 10,000-word document to extract three relevant facts, you are paying for 9,997 words of noise.
- A finance agent that reads your full marketing strategy before generating an invoice is wasting tokens.
- A customer service agent that loads your entire technical build history before replying to a complaint produces slower, noisier responses.
Specific context architecture reduces that noise to near zero. Each agent loads a small, targeted set of files. The model is not searching, it is applying. When the context is tight and accurate, outputs are specific to your business rather than plausible for any business: a customer reply that uses your actual pricing, your actual procedures, your actual tone. A cash flow report that reflects your actual cost structure. An invoice that matches your exact pricing rules.
Broad context gives you outputs you have to rewrite. Specific context gives you outputs you can use.
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How does multi-agent AI work inside this architecture?
A single agent handles one function well. Multi-agent AI is what happens when several specialised agents work together to complete a process end to end.
Each agent in the system has its own workspace, its own context files, and its own specific skills. When a task requires multiple functions, the agents hand work between themselves, the output of one becomes the input of the next. No human needs to coordinate that handoff.
Here is a concrete example. A new client enquiry arrives:
- The Research Director agent reads the enquiry, pulls available intelligence on the company, and qualifies the lead against your criteria.
- The Sales Director agent picks up the qualified lead, checks the CRM for prior contact, and drafts a personalised response.
- The Compliance Director agent reviews the outgoing message for regulatory or legal risk.
- The Admin Director agent logs the interaction, updates the pipeline, and schedules a follow-up.
Each agent only loads what it needs for its specific step. The workflow completes without a human touching it. The output quality is high because each agent worked with precise, relevant context at every stage. This is the difference between one generalist doing everything passably and a team of specialists each doing one thing well.
What does Anaboo actually build for your business?
Anaboo's agentic and multi-agent AI service designs and deploys this architecture inside your specific business. The process starts by mapping your actual processes, not theoretical workflows, but the real work that happens daily: who does what, what information they need to do it, where the handoffs happen, and where the time goes.
That mapping informs exactly how the agent workspaces and context files get built. Each department gets its own agent workspace. Each agent gets a CONTEXT.md that routes it to the right files for the right tasks. Each process that crosses departments gets a defined handoff point so agents can pass work between themselves cleanly.
The deployment methodology is built into how the system gets constructed, not a separate service layer, but the process by which the architecture gets deployed properly: understanding the business, onboarding the team, extracting the knowledge base, connecting the data, designing the decision logic, deploying the automation, and maintaining it over time.
What you end up with is a set of agents that know your business the way a well-briefed team member does. They do not guess. They do not pad outputs with generalities. They load what they need, do the work, and produce results that are specific to you.
What is AIOS and how does specific context architecture power it?
AIOS is a dedicated AI system, a separate computer running Claude's LLM and local models, kept on 24/7. It spins up agents with the right context for each task, hands off to the next agent once done, and allows a human to approve work at key checkpoints.
Specific context architecture is the design principle that makes AIOS accurate rather than approximate. Without it, the system would still run, but it would guess. With it, every agent in the system has a precise brief before it starts work, and the human in the loop only needs to step in at approval gates, not to fix vague outputs.
Who is this built for?
Established business owners who already have operations running, people in roles, and processes that work. The bottleneck is not capability, it is the volume of low-level, repeatable work sitting on your team's plate and on yours.
The specific context architecture works because there is real business context to work with: your pricing, your procedures, your voice, your client history, your data. The more specific your business context, the more precisely the agents can act on your behalf. Anaboo works with businesses across any industry, with up to 200 employees, in any geography. The common thread is an owner who wants AI doing the operational work accurately, rather than approximately.
What to do this week
- Audit one AI tool you use regularly. Ask: how much context does it load before responding? Is any of it irrelevant to the task at hand? If you cannot answer that question, the tool is probably guessing.
- Map one repeatable process in your business. List every step, who does it, what information they need, and where they hand it off. That map is the foundation of a specific context workspace.
- Book a free AI audit. It is more of a conversation than a formal audit, Anaboo looks at where your time goes, which processes are repeatable, and which parts of your business would benefit most from an agent with specific context rather than a general AI tool that guesses. The minimum guarantee is five hours a week saved.
Where to from here
Book a free 60-minute AI audit, we'll explore exactly what workflows are worth augmenting with AI.
Live with passion & AI,
Brett
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Frequently asked questions
Why does my AI give vague or generic answers?
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The most common reason is broad context. When an AI is handed a large document, or a pile of documents, it has to search through everything to find what is relevant, filling gaps with general training knowledge rather than your specific business knowledge. The result is outputs that feel almost right but miss the details that actually matter.
What is specific context architecture?
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Specific context architecture is a system design approach where each AI agent loads only the files and information it needs for its current task, and nothing else. Jake Van Clef developed the file structure Anaboo uses: each business area lives in its own workspace with a CONTEXT.md routing file that directs the agent to the right information for each task.
How does specific context architecture reduce AI costs?
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Tokens are the currency of AI interaction, every word the model reads costs money. When an agent loads a 10,000-word document to extract three relevant facts, you are paying for 9,997 words of noise. Specific context architecture routes each agent to a small, targeted set of files, reducing that noise to near zero.
How does multi-agent AI use specific context architecture?
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In a multi-agent system, each specialist agent has its own workspace, context files, and skills. When a new client enquiry arrives, for example, a Research Director agent qualifies the lead, a Sales Director agent drafts the response, a Compliance Director agent reviews it, and an Admin Director agent logs it, each loading only what it needs for its step, with no human coordinating the handoff.
What is the difference between a generalist AI tool and a specifically architected agent?
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A generalist AI tool produces outputs that are plausible for any business. A specifically architected agent produces outputs that are accurate for your business, using your actual pricing, your actual procedures, your actual tone, because it has been routed to your specific context rather than asked to guess.
Who benefits most from specific context architecture?
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Established business owners who already have people in roles and processes that work. The architecture requires real business context to function, your pricing, procedures, client history, and data. The more specific your context, the more precisely agents can act on your behalf. Anaboo works with businesses of up to 200 employees across any industry and geography.
What is AIOS and how does specific context architecture fit into it?
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AIOS is a dedicated AI system, a separate computer running Claude's LLM and local models 24/7, that spins up agents with the right context for each task, hands off to the next agent once done, and allows a human to approve work at key checkpoints. Specific context architecture is the design principle that makes AIOS accurate rather than approximate.
Brett is a four-time founder (Darra Tyres, Gladfish, EzyTrac, Anaboo) and the operator behind AIOS, Anaboo's AI Operating System. He writes from inside the build, installing AI in his own businesses first and reporting back what actually moves the numbers. Based between Singapore, the UK and Australia.
