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Brian Koech
Brian Koech

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Standard-Bearer AI: Making Company Standards Conversational

#devchallenge #algoliachallenge #ai #agents

This is a submission for the Algolia Agent Studio Challenge: Consumer-Facing Conversational Experiences

Demo

What I Built

Project Link:
Live Demo: https://algosearch-alpha.vercel.app/
GitHub Repository: https://github.com/koechkevin/algo

Diagram showing the Retrieval-Augmented Generation (RAG) workflow: User query leads to Algolia Search, which retrieves context from the documentation index to feed the AI Agent for a grounded response.

*The Problem *

🛡️
In my experience as a developer, I've seen internal coding standards buried in static PDFs, Confluence pages, or READMEs that nobody reads. This leads to "technical debt by accident"—developers want to follow the rules, but finding them is a chore. When information "foraging" is too hard, consistency dies

*The Solution: Standard-Bearer AI *

🤖
Standard-Bearer AI transforms stagnant documentation into a proactive architectural mentor. Built using Algolia Agent Studio, it allows developers to ask natural language questions and receive precise, company-sanctioned answers instantly.

Instead of browsing through a list of search links, the developer gets the exact answer extracted from the source of truth

How I Used Algolia Agent Studio

1. The Knowledge Base (Algolia Index)
I created a structured JSON index containing core standards for:

  • Python Style Guides (PEP 8)
  • Conventional Commits
  • Security & API Key Management
  • Database Migration Policies

2. The Brain (Agent Studio)
Using Algolia Agent Studio, I configured a "Standard-Bearer" agent. I implemented a strict System Prompt to ensure the AI acts as a "source of truth" guardian. It is instructed to only answer based on the indexed data, effectively eliminating hallucinations

3. The Experience (Frontend)
The frontend is a modern Vite application using InstantSearch.js. I integrated the new InstantSearch chat widget, which provides the conversational bridge to the Agent.

Auto-open Logic: The widget automatically greets the developer on load, reducing the friction to start a query.

Visual Cues: I added a custom mutation observer to show animated loading states while the AI is "thinking" and retrieving documentation

Why Fast Retrieval Matters

In a documentation context, latency is the enemy of adoption. If a developer has to wait 10 seconds for a search result or an AI response, they will simply stop using the tool and go back to guessing—or worse, following outdated patterns

**Reliable RAG (Retrieval-Augmented Generation): Fast retrieval allows the Agent to perform multiple lookups if necessary, ensuring that the AI’s response is always grounded in the most relevant, up-to-date documentation

Technical Architecture *🧠
*User Query: "How should I name my Python classes?"

Retrieval: Algolia searches the internal_documentation index for the "Python Style Guide" record.

Augmentation: The text "Use PascalCase for classes" is fed to the Agent.

Generation: The Agent Studio LLM synthesizes a helpful response: "According to our standards, you should use PascalCase for classes."

Technical architecture diagram of Standard-Bearer AI, illustrating the connection between the Vite frontend, Algolia Agent Studio, and the internal documentation index.

Team Members:
@brykoech254 Brian Koech

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