The Architecture of Autonomous Business Work.
Orbion Technologies is engineered to bridge cognitive foundation models with deterministic enterprise execution. Explore our core technical pillars, runtime protocols, and the distinction between our active prototypes and long-term research direction.
Enterprise Intent & Telemetry
Webhooks, API events, scheduled cron jobs, and human prompts.
Cognitive Orchestration Runtime
DAG formulation • Multi-agent planning • Hierarchical context assembly • Pre-execution safety filters.
Model Context Protocol (MCP) Standard
Type-safe JSON-RPC client-server contracts with sandboxed tools.
Enterprise Actions & Ledger
Database mutations, CRM updates, code commits, and verified audits.
Core Technical Pillars
Autonomous AI Agents
Goal-directed execution loops that decompose high-level business objectives into planned sub-tasks, evaluate intermediate states, and recover gracefully from runtime exceptions.
- •Goal decomposition via structured planning phases
- •Stateful execution ledgers with continuous rollback checkpoints
- •Self-correcting error handling loops and retry policies
Tool Calling & Model Context Protocol (MCP)
Implementation of Anthropic’s open Model Context Protocol (MCP) as the primary interface between cognitive agents and enterprise data assets, APIs, and file systems.
- •Standardized JSON-RPC 2.0 client/server adapters
- •Strict schema validation using JSON Schema and TypeScript typings
- •Fine-grained access controls and least-privilege tool scopes
Deterministic Workflow Orchestration
Replacing chaotic, unpredictable prompts with Directed Acyclic Graphs (DAGs) to coordinate multi-agent dependencies, concurrency, and validation checkpoints.
- •Parallel DAG branch execution with dependency resolution
- •Explicit verification barriers before state-mutating actions
- •Deterministic replayability and reproducible audit traces
Hierarchical Memory Architecture
Multi-tiered memory persistence designed to overcome LLM context-window limits while preserving historical organizational context across long time horizons.
- •Turn Buffer: Ephemeral conversational tokens for local context
- •Working Memory: Active task DAG state, active tool outputs, and ledger
- •Episodic Memory: Compressed historical execution traces across sessions
- •Semantic Memory: Vectorized knowledge graph and enterprise documentation
Hybrid Retrieval-Augmented Generation (RAG)
Combines dense semantic vector retrieval with sparse keyword indexing and graph-based entity linking to ground agent actions in factual organizational truth.
- •Dynamic semantic chunking tailored for code, tickets, and tabular data
- •Hybrid BM25 + dense embedding re-ranking pipeline
- •Source citation and provenance tracking for every generated assertion
Multi-Agent Swarm Dynamics
Dividing complex workflows among specialized agents (Planner, Operator, Verifier, Safety Gate) that cross-examine proposals before actions are committed to production systems.
- •Role-specialized personas with isolated system prompts
- •Multi-agent consensus protocols to mitigate single-model hallucinations
- •Human-in-the-loop escalation paths for high-risk operations
Current Capabilities vs. Long-Term Research
We maintain absolute transparency regarding what is built and validated in prototypes today versus what constitutes our forward-looking research agenda.
MCP Integration Testbed
Testing standardized MCP servers connecting agents to local file systems, SQLite databases, and mock REST APIs.
Deterministic Tool Dispatch
Benchmarking strict schema enforcement using Pydantic and TypeScript validation before tools are invoked.
Task Decomposition Kernels
Evaluating DAG-based planning prompts that convert vague user prompts into verifiable step sequences.
Local Context Compaction
Algorithmic summarization of multi-turn tool interaction history to avoid context window degradation.
Ephemeral Micro-VM Isolation
Sandboxed sub-second micro-virtual machines (Firecracker/gVisor) for executing arbitrary code generated by agents.
Cross-Enterprise Agent Federation
Secure cryptographic protocol enabling autonomous agents from different organizations to negotiate and transact.
Continuous Streaming Vector Bus
Sub-second semantic vector indexing of enterprise Kafka feeds, database change streams, and communication webhooks.
Self-Tuning Execution DAGs
Reinforcement learning frameworks that optimize multi-agent workflow paths based on historical latency and cost telemetry.