🧠
Entity Memory
Stores relevant observations, past actions, relationship context and decision history.
Veritiana Labs / Autonomous Entity Research
Aura Entities. Simulated agents with memory, signals and behavior.
GITHUB:https://github.com/veritiana/aura-agent-sim
Aura Entities is a Veritiana Labs project for exploring how autonomous digital entities can observe signals, keep memory, build context and make controlled decisions inside a simulated environment.
Signals
observed state
Memory
entity context
Action
decision output
Entity model
Observe → remember → decide → act
A research layer for testing agent behavior before applying it to production business workflows.
Purpose
A lab for understanding agent behavior before production deployment.
Aura Entities is not a customer-facing chatbot. It is a controlled simulation environment for studying how AI entities process context, react to signals, form memory and choose actions over time.
Architecture
Entity behavior as a controlled pipeline.
The system separates perception, memory, decision logic and action. This keeps the agent explainable instead of turning behavior into uncontrolled prompt output.
👁️
Observe
The entity reads environmental signals, events and state changes.
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Remember
Relevant events are stored as memory with context and priority.
⚖️
Evaluate
The entity scores possible actions against goals, rules and current state.
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Decide
A decision is selected through controlled logic instead of free-form improvisation.
⚡
Act
The entity changes state, communicates or triggers a simulated event.
Why it matters
AI agents need behavior models, not only language models.
Large language models can generate text, but real agent systems need persistent memory, state awareness, decision boundaries and measurable behavior. Aura Entities explores these foundations in a visible simulation layer.
Persistent context
Entities carry memory across events instead of reacting as stateless prompt calls.
Signal-based decisions
Actions are driven by observable inputs, weights, thresholds and environment state.
Controlled autonomy
The system tests autonomy inside defined boundaries, rules and evaluation criteria.
Production learning
Research feeds back into Answer Engine, Signal and SourceNote architecture.
Core modules
What Aura Entities experiments with.
📡
Signal Engine
Converts environmental state into weighted inputs that can influence decisions.
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Decision Model
Tests how agents choose actions based on goals, rules, constraints and context.
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Environment State
Defines places, objects, relationships, events and state transitions.
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Behavior Log
Records why the entity acted, which signals mattered and what changed afterward.
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Experiment Layer
Runs scenarios to compare memory rules, signal weights and action policies.
Labs status
Research concept for future autonomous workflows.
Aura Entities belongs in Labs. Its role is to test and visualize behavioral principles that can later strengthen production systems: verified answers, product signals and operational agents.
Connection to Veritiana
From lab simulation to business agents
Answer Engine
Uses controlled context and answerability decisions.
Uses controlled context and answerability decisions.
Signal
Uses structured signals instead of ambiguous product pages.
Uses structured signals instead of ambiguous product pages.
SourceNote
Uses agents to turn messy operational input into verified records.
Uses agents to turn messy operational input into verified records.
Aura Entities
Tests memory, behavior and decision dynamics before production use.
Tests memory, behavior and decision dynamics before production use.