Systemiq
Operational context and memory for AI agents.
Systemiq turns fragmented enterprise signals into modeled operational context and memory that agents can query instead of reconstructing themselves.
How it fits
Enterprise signals
APIs, files, events, business tools, and changing operational reality
Systemiq
System modeling, operational context, and memory
AI agents
Reasoning, decisions, and execution from shared context
Systemiq sits between enterprise systems and downstream agents, carrying the burden of modeling, continuity, and queryable operational memory.
How the model forms
Systemiq structures enterprise signals into a layered operational model. Elements capture local state, tools form subsystem context, systems define major operational boundaries, client-level interpretation can span across systems, and derived outputs preserve what agents need over time. One useful way to describe that connected structure is as a context graph.
Enterprise signals
Systemiq selects operational evidence from across the stack instead of treating every raw source as agent input.
- APIs
- Business systems
- Documents
- Events
- Transactions
- And more selected evidence...
Selected evidence enters the model. Agents do not need to reconstruct it from scratch.
Systemiq model
The model translates raw enterprise signals into a queryable operational structure with persistent memory.
Elements
Atomic nodes, local state, and indicator evidence.
Tools
Subsystem context and modeled local behavior.
Systems
Operational boundaries, rollups, and propagated interpretation.
Client
Cross-system interpretation, summaries, and shared context.
Persists as
Context graph examples
See how the modeled context is structured.
Agent access
Agents query modeled context directly through stable access surfaces instead of rebuilding reality from raw systems.
Which systems matter now?
What changed since the last run?
Where is impact rising or propagating?
Access surfaces