Conversational AI Agent

Your Natural Language Gateway to DeFi

Component Classification: LLM-powered natural language processing and orchestration interface within the aarnâ Multi-Agent Architecture.

1. Functional Purpose

The Conversational Agent acts as the semantic interface layer between user intent and system-level configuration logic. It converts natural language inputs into structured, machine-interpretable command objects and governs the decision pipeline before execution. The Conversational Agent ensures that all user-driven actions (configuration, insight queries, transaction intent, or reasoning requests) are processed deterministically and aligned with protocol rules, safeguards, and vault risk constraints.

2. Core Capabilities

Intent Classification & Semantic Parsing

• Utilizes transformer-based LLM inference for:

  • Intent detection

  • Slot/entity extraction

  • Constraint identification (risk thresholds, asset boundaries, timeline constraints).

• Normalizes ambiguous expressions (e.g., "maximum safe yield" or "higher returns but low volatility") into system-defined feature vectors.

Policy-Aligned Strategy Mapping

• Converts parsed intent into validated vault configuration instructions.

• Applies:

  • Risk framework lookups

  • Eligibility matrices

  • TVL ceilings

  • Chain availability constraints

  • Tactical vs strategic sleeve logic.

• Generates configuration instructions for downstream agents without violating governance or execution rules.

Execution Mediation

• The Conversational Agent does not directly trigger blockchain transactions.

• Instead, it:

  • Generates execution signatures,

  • Confirms user approval,

  • Passes final instruction packets to the Execution Agent.

3. Memory + Personalization Layer

• Persistent, wallet-scoped memory (no identity leakage).

• Uses vector embeddings + semantic recall rather than rule-based storage.

• Supports session memory (short-term reasoning) and long-tail preference modeling.

Memory examples stored as feature vectors:

• Risk preferences

• Typical asset exposure

• Interaction patterns (active vs passive user)

• Prior allocations and rebalance approvals

4. Communication Model

The Conversational Agent supports:

• User-initiated flow (queries, instructions)

• System-initiated flow (alerts, deviation signals, yield opportunity push)

Communication signals include:

• Risk shift notifications

• Rebalancing triggers

• Yield deltas > threshold

• PT opportunity upgrades (from Yield Curation Agent)

All messaging is generated in natural language but anchored in a quantifiable system state.

5. Guarantees & Guardrails

Domain	Enforcement
Safety	Risk policy enforcement, transaction sanity checks
Consistency	Deterministic mapping of preferences → configuration
Explainability	All actions include natural language reasoning
Non-Custodial Integrity	No execution without explicit approval