Multi-Vector Image Search, Kiro’s Real Role & TOON vs .txt | Weekly Agentic AI Deep Dive

In this session we blend weekly AI news, cutting-edge retrieval techniques, and a thoughtful discussion of emerging formats for structured AI outputs.

We’ll start with this week’s AI news, then dive into a hands-on look at our multi-vector image retrieval work (in our repo), a powerful prerequisite for anyone building smarter RAG, search, or multimedia agents. You’ll see how going beyond a single embedding unlocks richer, more accurate image search.

Next, we turn to Kiro and tackle the big question:

Is Kiro a coding agent or an IDE?

Our short answer: Kiro is not an IDE in the traditional sense — it’s a coding agent that lives inside an IDE. We’ll unpack what that means in practice: how Kiro orchestrates agents, integrates with your editor, and helps move from “vibe coding” to more structured, spec-driven development.
AWS Builder Center
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Finally, we dig into Token-Oriented Object Notation (TOON) and compare it with .txt / Outlines style structured generation. TOON is a compact, human-readable encoding of the JSON data model designed specifically for LLM prompts, aiming to reduce tokens while preserving structure.
GitHub
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ToonFormat
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By contrast, tools from dottxt.ai focus on constraining models to emit strictly structured outputs that match schemas or grammars, regardless of the surface notation.
Dottxt
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Dottxt
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GitHub
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We’ll use these as talking points to discuss how you should think about prompt formats, schemas, and structured generation in your own agentic systems.

What We’ll Cover

📰 This Week’s News

A short roundup of notable AI stories and releases.

How these changes affect builders working with agents, RAG, and tooling.

🖼️ Multi-Vector Image Retrieval (In Our Repo)

Why a single embedding per image is often not enough.

How multi-vector (multi-region / multi-concept) embeddings improve recall and relevance.

Where to use this: visual RAG, multimodal agents, semantic search over screenshots, diagrams, or UIs.

💻 Kiro – Coding Agent or IDE?

The real answer: Kiro as an agentic coding layer inside an IDE, not a traditional editor.
AWS Builder Center
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How Kiro decomposes specs, coordinates tools (via MCP), and helps take projects from prototype to production.
AWS Builder Center
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TechRadar
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When you’d reach for Kiro vs. other coding assistants or standard IDEs.

📘 Token-Oriented Object Notation (TOON): First Impressions

What TOON is: a compact, human-readable, lossless encoding of the JSON data model for LLM prompts, designed to reduce token count while keeping structure easy for models to follow.
ToonFormat
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Pros & cons: token savings vs. readability, tooling, and community adoption (including some emerging critiques).
Reddit

📐 Comparing TOON to .txt / Outlines

TOON: focused on how data is serialized for the model (format efficiency over JSON).
ToonFormat
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.txt / Outlines: focused on controlling what the model is allowed to output, guaranteeing structured, schema-compliant responses using grammars and typed schemas.
DotTXT Blog
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Dottxt
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GitHub
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Talking points:

When to optimize prompt format (TOON/JSON/etc.) vs. when to enforce output structure (.txt/Outlines, JSON Schema, grammars).

How these choices affect reliability, latency, and cost in production agentic systems.

Resources

Multi-Vector Image Retrieval (Repo): (Add your repo link here)
TOON Spec & Site: https://toonformat.dev/

ToonFormat

TOON GitHub: https://github.com/toon-format/toon

GitHub

.dottxt / Outlines: https://dottxt.ai/

Dottxt
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Kiro Overview (AWS Builder article):
AWS Builder Center

Host: Mark Kerzner (  / markkerzner  
)
ElephantScale Webinars: https://elephantscale.com/webinars/

Keywords

AI News, Multi-Vector Image Retrieval, Image Search, Vector Search, RAG, Kiro, AWS Kiro, Agentic AI, Coding Agent, IDE, Token-Oriented Object Notation, TOON, Structured Generation, .txt, Outlines, JSON Alternatives, MCP, LLM Prompt Formats, Mark Kerzner, ElephantScale.

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