Ideas explored in depth.
Everyone's building AI agents. Almost no one is building what agents actually need to work in the real world — persistent memory, security boundaries, cross-organizational trust, and economic coordination.
Pyramidal builds that missing layer. Open protocols, a runtime platform, and domain-specific intelligence — designed to work together.
Language models can reason. But reasoning alone isn't enough to act safely in the world. The gap between a chatbot and a trusted autonomous agent is an infrastructure problem.
Most AI tools start from zero every session. They don't remember what they learned yesterday, what they promised last week, or what worked three months ago. Intelligence that doesn't compound isn't intelligence — it's expensive pattern matching.
When an agent is compromised — and they will be — there's usually nothing limiting the blast radius. No isolation between agents, no boundaries between tenants, no architectural guarantee that a failure in one place won't cascade everywhere.
Your agent can't discover my agent, verify its identity, negotiate permissions, or collaborate on a shared task. There's no common language for agents from different organizations to find and trust each other.
Agents that do real work need to manage costs, hold value, and build reputation. Today, agent economics are an afterthought bolted onto human payment systems that weren't designed for machine-to-machine commerce.
Most companies pick one piece of the puzzle. We believe these layers only work when they're designed together — so we're building all three.
Standards that anyone can adopt — for how agents query data, coordinate across organizations, federate trust, and transact value. Open by design, not by afterthought.
A runtime where agents operate safely — with persistent memory, security isolation, structured coordination, and full cost transparency. The infrastructure you'd have to build yourself, already built.
Vertical agents that prove the stack works. Each one brings deep domain expertise — research, analytics, manufacturing, personal management — powered by knowledge graphs that learn over time.
Adaptive Decentralized Architecture for Multi-Agent Systems
ADAMAS handles the hard infrastructure problems — isolation, memory, coordination, and accountability — so you can focus on what your agents productively do, not minimizing their risks to your business.
The result is a new kind of role: the Architect-Orchestrator — someone who directs fleets of autonomous agents instead of running them ad hoc. The same people, producing 10–50x the output.
Knowledge graphs that persist across sessions. Every execution, every decision, every learned pattern becomes part of an agent's growing intelligence — searchable, connected, and cumulative.
The security model assumes every agent will eventually be compromised. When that happens, architectural isolation limits the damage — a compromised agent can't reach other agents, other tenants, or the broader system.
Patterns for multi-agent workflows — fan-out, fan-in, conditional branching, iterative refinement — with durable execution that survives crashes and handles long-running tasks without timeouts.
Cost tracking at every layer: tokens, operations, execution time. Complete audit trails for every action. When an agent makes a decision, you can trace exactly how it got there and what it cost.
Open specifications designed for adoption beyond Pyramidal. We use them. You can too.
A protocol for exposing any business domain as a queryable graph. Instead of giving agents dozens of API endpoints to figure out, DGP presents your data the way agents think — as entities, relationships, and traversals. One protocol, any domain.
More protocols in development:
TURN — Temporal Unified Resource Notation ·
AFP — Agent Federation Protocol ·
ZKA — Zero-Knowledge Agents ·
TVF — Trust Verification Framework ·
VXP — Value Exchange Protocol
Domain agents, developer tools, and interfaces — each built on the ADAMAS platform and DGP protocol.
Ideas explored in depth.