Standpoint Labs on StandpointLabs

The Flashlight Problem

Sun, 25 Jan 2026 09:00:00 -0700

Why coordination fails even when everyone does their job.

They didn’t send amateurs into the cave.

Everyone was experienced. Careful. Each carried a powerful flashlight and knew the rules: call what you see, move with intention, don’t rush the dark.

At first, everything worked. One beam caught a fracture in the ceiling. Another flagged a drop in the floor. Each warning was precise. Each adjustment reasonable.

That’s how it broke.

Why Can't You Just Prompt Better?

Tue, 06 Jan 2026 09:00:00 -0700

The question we hear most often: “Can’t you just give agents better prompts?”

Short answer: No.

Longer answer: Prompting is necessary but not sufficient for coordination.

Here’s why:

The prompt fallacy: You can tell an agent “consider other stakeholders” but it doesn’t know what those stakeholders care about unless you spell it out. Every time. For every stakeholder. Updated constantly as things change.

The context window limit: Even if you could spell out every stakeholder’s perspective in every prompt, you’d blow through context limits on any non-trivial organization.

Looking for Design Partners

Sat, 20 Dec 2025 14:00:00 -0700

We’re actively looking for design partners for Standpoint Core. Here’s what we’re looking for:

Ideal design partners:

Running multiple AI agents that need to coordinate
Experiencing the “agent babysitter” problem firsthand
Willing to provide feedback and iterate with us
Not expecting polished enterprise software (yet)

What you get:

Early access to Standpoint Core
Direct line to the founding team
Influence on product direction
Pricing that reflects early-stage partnership

What we get:

Introducing the Mesh Concept

Thu, 04 Dec 2025 15:00:00 -0700

Standpoint Core models perspectives. But modeling isn’t enough - systems need to act on that understanding.

Enter: Standpoint Mesh.

The Mesh is the collaboration layer that sits on top of Core. While Core answers “what does this situation mean from each perspective?”, Mesh enables “how do we coordinate action given those different perspectives?”

Key principles emerging:

Boundaries matter. Not everything should be shared with everyone.
Context must be preserved across handoffs.
Each participant’s concerns must be respected, not just acknowledged.

The two products are starting to feel like a coherent whole. Core provides understanding, Mesh enables action.

Mapping the Context Graph Landscape

Sat, 15 Nov 2025 11:00:00 -0700

Spent this week mapping the emerging context graph landscape. Every major AI company is racing to add organizational context to their systems.

What they’re building:

Knowledge graphs of company data
RAG systems with enterprise documents
Agent memory and context windows

What they’re missing:

How the same information means different things to different parties
Implication tracking across perspectives
Coordination beyond information sharing

This gap is exactly where Standpoint fits. We’re not competing with context graphs - we’re completing them.

Perspective Modeling Breakthrough

Tue, 28 Oct 2025 14:00:00 -0700

Major breakthrough this week. The key insight: relationships aren’t symmetric in practice, even when they appear symmetric in structure.

Consider: Manager A and Report B are connected by an employment relationship. Structurally symmetric. But what’s at stake for each party is completely different. What they notice, what they worry about, what they optimize for - all different.

Current graph databases and knowledge systems treat connections as edges with properties. They miss this entirely. The edge is the same from both directions. But meaning isn’t.

Initial Research Phase

Mon, 13 Oct 2025 10:00:00 -0700

Starting the formal research phase for what would become Standpoint Labs. The question driving this work: why do AI systems struggle to coordinate, even when they have access to the same information?

Early hypothesis: the problem isn’t access to data - it’s understanding what that data means to different parties. Two systems can see the same connection but interpret its importance entirely differently.

Spending this month reviewing existing literature on multi-agent coordination, organizational theory, and relational modeling. Building the theoretical foundation before writing any code.

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