Thomas Davis thomasdavis

Dynamic Tool Selection for AI Agents

A guide to implementing per-step tool filtering using ensemble scoring. This approach solves the problem of LLMs having too many tools to choose from effectively.

The Problem

When you give an LLM agent 40+ tools, several issues emerge:

Context bloat: Tool definitions consume tokens, leaving less room for actual conversation
Selection confusion: LLMs struggle to pick the right tool when faced with many similar options

Thanks! I’ll explore potential bottlenecks and failure modes of your current AI summary tooltip design when handling large AG Grid datasets (10,000 to 1 million+ rows), and then provide a range of strategies to make it scale effectively without sacrificing UX or LLM performance. I’ll also consider architectural and LLM prompt/streaming optimizations tailored for your Vercel + OpenAI setup.

I’ll report back shortly with practical options.

Scaling LLM-Based Summaries for Large Datasets

Introduction: Implementing an AI summary tooltip in a data grid provides users with quick insights, but scaling this to massive datasets (tens of thousands to millions of rows) introduces significant challenges. The current approach – sending all active column values to an OpenAI model via the Vercel AI SDK – works for small tables, but breaks down as data grows. We need to analyze where this design fails at scale, and explore both frontend and backend strategies to make the summary feature robust for any dataset si

Thanks! I’ll design a full, modular pipeline that builds a Deep Research system like OpenAI’s but customized to your analyzer stack. It will:

Use your analyzer output to deeply understand the user's query and tone
Decompose complex or multi-part prompts into subquestions using QDMR-style logic
Auto-generate research plans with prioritized search queries, tailored to the topic
Use Firecrawl/Perplexity APIs to retrieve high-signal results, emphasizing academic sources
Loop over search + synthesis, revising the plan if the evidence doesn’t satisfy the question
Generate sectioned Markdown reports with structured citations, tables, graphs where needed
Embed inline source links with strategies to future-proof them (e.g., reverse RAG or citation mirrors)

Great, I’ll look into academic and practical frameworks for formally breaking down user questions and intent, drawing from linguistics, epistemology, ontology, and NLP. This will include theories of meaning, discourse analysis, question decomposition, and related computational approaches.

I’ll let you know as soon as I have a structured summary of the best-supported methodologies and tools.

Formal Frameworks for Analyzing User Intent in Natural Language

Introduction

Understanding a user's intent from a natural language query often requires decomposing the utterance into formal components of meaning. Consider the example: "Tell me if the US elections affected the Canadian ones?" – This question contains an imperative request ("Tell me...") and an embedded yes/no query about causality between two events. To analyze such an utterance, one must identify the speech act (a request for information), the semantic content (whether U.S. elections had an effect on Canadian elections), and the impli

Below is a practical recipe + Node .js scaffolding for turning your 250-page Markdown dump into three linguistics-aware assets:

CLDF StructureTable – grammar-feature spreadsheet
XIGT (JSON) – interlinear examples
OntoLex-Lemon (JSON-LD) – lexicon entries

The same pattern works for any other schema; just swap the “output-format” system message.

Below is a “from-PDF-to-production” blueprint that lets you pour the entire Grammar of Kuku Yalanji into a single modern stack – relational tables for precision, a vector index for AI search, and a graph/RDF layer for linked-data reuse.

1 Why three layers?

Layer	What it gives you	Typical tech
Relational / JSON B	Loss-less storage of paradigms, rules, example IDs; fast SQL & GraphQL	PostgreSQL 16
Vector index	Semantic retrieval for RAG (“find the paragraph that explains ergative case”)	`pgvector` inside Postgres OR an external DB like Weaviate citeturn0search2turn0search3

A grammar of the Kuku Yalanji language of north Queensland

Also in Pacific Linguistics

Mark Harvey, 2001, A grammar of Limilngan: a language of the Mary River region, Northern Territory.
Jane Simpson, David Nash, Mary Laughren, Peter Austin and Barry Alpher, 2001, Forty years on: Ken Hale and Australian languages.

David Rose, 2001, The Western Desert code: an Australian cryptogrammar.

L.A. Hercus, 1999, A grammar of the Wirangu language from the West Coast of South Australia.

Perfect — I’ll begin crafting a complete design system for PrecisionCore tailored for web applications that work well on both desktop and mobile. It’ll default to light mode, include dark mode support, and cover all essential components found in modern web apps.

The system will include:

In-depth design philosophy and visual principles
Guidelines for layout, spacing, grids, and UI behaviors
Component breakdowns with usage rules
Code examples in vanilla HTML/CSS (primary) and TailwindCSS (secondary)
Mockups and image examples of components in the PrecisionCore style

I’ll let you know as soon as it’s ready for review.

	{
	"basics": {
	"name": "Thomasaasd asda",
	"label": "Invenasdtbosar andasd Businessman",
	"picture": "https://example.com/photo.jpg",
	"email": "[email protected]",
	"phone": "(123) 456-7890",
	"website": "https://thomassdedison.com",
	"summary": "asdasdsasdsdsProlific inventor and businessasdasdman known for developing many deviasdasdsces that greatly infasasluenced life around the world, including the phonograph, the motion picture camera, and the electric light bulb.",
	"location": {

	---
	config:
	layout: fixed
	---
	flowchart TD
	n1["Message Received"] --> n2["Intent Prompt"]
	n2 --> n3["No Tool"] & n5["Found Tool"]
	n3 --> n6["Stream MainPrompt"]
	n5 --> n7["GetToolStrategy"]
	n7 --> n8["NoToolStrategy"] & n10["FoundToolStrategy"]