Pyramidal designs the protocols, platforms, and domain intelligence that agents need to operate safely, learn continuously, and collaborate across boundaries.
Agents that can act in the world need more than a language model. They need isolation, memory, identity, economics, and trust. None of that comes from the model itself.
Foundation models provide reasoning. Everything else — the architecture that makes reasoning safe, persistent, and economically viable — remains unbuilt. Pyramidal builds that architecture.
Prompt injection attacks will always succeed. Security must come from architectural isolation — limiting what a compromised agent can damage — not from input filtering.
Stateless LLM calls discard everything between sessions. Agents need memory that compounds: learning from past executions, accumulating domain knowledge, improving over time.
Agents that transact, hold value, and build reputation need privacy-preserving payment protocols and verifiable coordination — not retrofitted human payment rails.
Agents from different organizations, built on different frameworks, need to discover each other, negotiate trust, and collaborate without a central authority.
Adaptive Decentralized Architecture for Multi-Agent Systems
ADAMAS treats AI agent orchestration as an infrastructure problem, not a prompt engineering problem. Agents execute in Firecracker microVMs — the same technology powering AWS Lambda — providing hardware-level isolation through the KVM hypervisor. A compromised agent can't escape its VM, can't reach the network, can't access other tenants.
The Atomic Knowledge Graph gives agents persistent, semantic-searchable memory that accumulates across sessions. Durable workflows survive crashes and handle long-running tasks without timeouts.
Firecracker microVMs provide a KVM hypervisor boundary. A kernel exploit compromises the guest, not the host. The security model assumes the agent is already compromised.
The Atomic Knowledge Graph stores every execution as searchable atoms. Agents discover capabilities at runtime through progressive disclosure, not frontloaded tool definitions.
Structured patterns for multi-agent coordination — fan-out, fan-in, conditional branching, reasoning-guided iteration — powered by Restate's durable execution.
Cost tracking at every layer: tokens, operations, execution time. Full provenance through ExecutionAtom audit trails. Every action is accountable.
Open specifications designed for adoption beyond Pyramidal. Each defines how a dimension of agent infrastructure should work.
An agent-first protocol for exposing business domains as queryable graphs. Cost-transparent operations, semantic filtering, and relationship traversal designed for LLM consumption rather than human UIs.
dgp.dev →A notation system for multi-agent orchestration — the equivalent of musical scores for agent systems. Temporal coordination, concurrency, and synchronization made readable and composable.
turn.dev →ADAMAS is a horizontal platform. The agents that run on it are vertical — each with domain-specific knowledge graphs, workflows, and expertise.
Competitive analysis, market trend synthesis, customer intelligence, and due diligence. Satya accumulates and connects knowledge across time — the cumulative intelligence model vs. stateless research tools that forget everything between sessions.
satya.ai →Contextual support with long-term user memory, knowledge base integration, and intelligent escalation. Nishchinta tracks user history, preferences, and past interactions — creating ease through understanding, not scripts.
nishchinta.ai →Voice-first interface infrastructure. Agents dynamically render UI as conversational cards, with emotion-aware responses adapting to user context and tone.
hyper.talk →Benchmarking and cost optimization for AI model selection. Cross-provider performance comparisons and prompt-based recommendations for finding the right model.
model.shopping →Pyramidal's agents aren't organized by business function. They're grounded in the Trishakti — three fundamental powers through which consciousness engages with reality.
Each agent embodies a mode of consciousness. The Sanskrit names point to established categories in consciousness phenomenology refined over millennia — from Tantric philosophy, Kashmir Shaivism, and Vedānta. This isn't branding. It's a design language that determines how agents are built, how personality varies across contexts, and why each domain exists.
Satya (discernment) and Nishchinta (equanimity) are the first two modes — one from the knowing power, one from the willing power. As the agent roster expands, each new agent will embody another mode, filling out a framework where the three shaktis are mutually co-arising: you cannot act without knowing what to create, you cannot know without willing to attend.
Ideas explored in depth.