The Geometry of Intention - How LLMs Predict Human Goals

09 Nov 2025 • Facilitator: Performics Labs Newsroom

💡 TL;DR - This All-Hands builds on Memory & Agency to explore how LLMs infer human goals from structured context.
We’ll use this theory to prototype “Intent-Recognition Agents” for marketing - systems that understand why a user acts, not just what they click.
The article below is your foundation for the MCP Hackathon Winter 2025, where you’ll apply these ideas to build real-world, agentic applications.

🧭 From Memory to Intention

In Phenomenology of Search we studied the geometry of meaning.
In Memory & Agency we gave models a sense of continuity.
Now we shift to the human goal itself - how intention emerges, and how LLMs can model it through Context-Conditioned Intent Activation (CCIA).

Hypothesis:
LLMs can reliably define and predict human intention when and only when the prompt provides enough structured situational context to activate the latent sub-manifolds of human social-goal patterns.

Read the full deep-dive:
👉 The Geometry of Intention - How LLMs Predict Human Goals in Marketing Contexts

🛠️ Build-In-Public Sprint - “Intent Recognition Agents”

Goal: Prototype an MCP-compatible agent that can read behavioural context and infer user intention with confidence scoring.
You’ll connect theory to practice in one of two tracks.

🔧 Track A - Building MCP (Protocol Side)

Step	Deliverable	Hints
1	MCP Server for intent-classification	Base on Gradio 6 MCP docs
2	Context-Capture Schema	Use the 5-dimension template from the article
3	CCIA Prompt Template	Calibrate confidence > 0.7 = “High Intent”
4	Integration Test	Connect to Claude Desktop / Cursor via MCP
5	Demo Video (≤90 s)	Show live classification + reasoning trace

🤖 Track B - MCP in Action (Agent Side)

Step	Deliverable	Hints
1	Full agent app (Gradio Space)	Uses your MCP server or public tools
2	Intent Dashboard	Visualise confidence, context, next-actions
3	Pattern Discovery Module	Cluster 100+ sessions → archetypes
4	Activation Logic	Map intents → personalised responses
5	Demo Video (≤90 s)	Show end-to-end inference & adaptation

Recognition: Submit to Track 2 - MCP in Action of the official Hugging Face hackathon.
Internal showcase winners get featured in our December Labs Showcase.

🏆 Hackathon Details - MCP 1st Birthday (Hosted by Hugging Face + Anthropic)

Dates: Nov 14 – Nov 30 2025
Tracks: Building MCP · MCP in Action
Prizes: $15 000 + API credits + sponsor bonuses
Register: 👉 Join the Hackathon
Community: Discord → #agents-mcp-hackathon-winter25🏆

Why join?
MCP is now mainstream - adopted by OpenAI, Microsoft & Google DeepMind.
This is your chance to build agents that reason, plan, and infer goals using the Geometry of Intention framework.

✅ Acceptance Criteria

Context Conditioning - supply ≥ 4 context dimensions per inference.
Confidence Calibration - expose numeric certainty in output.
Intent Taxonomy - 5–8 clearly defined labels per domain.
Pattern Discovery - cluster ≥ 500 classified sessions.
Activation Demo - show one real campaign / UX action responding to intent.

Bonus

Visualization of latent intent space (2-D projection)
Integration with existing marketing data pipelines (GA4, DV360, Meta)
Reusable CCIA prompt library for MCP tools

💬 Discussion Prompts

How does “context-conditioned activation” differ from classic personalization?
Where should we draw the ethical line between prediction and manipulation?
Could intent-recognition agents cooperate - sharing patterns without leaking PII?
How might CCIA reshape search, programmatic, and e-commerce strategy in 2026?

Add your thoughts in the comments or our internal #intent-geometry channel.

📚 Reference Pack

🚀 Call to Action
Use the Geometry of Intention framework to design agents that infer, not assume.
Build your prototype during the MCP Hackathon Winter 2025 - and show the world how marketing systems can finally understand what people mean when they act.