AI Agents
KEK uses a system of specialized AI agents that coordinate to analyze markets, generate strategy candidates, and cross-validate each other's reasoning.
Each agent has a distinct reasoning domain. Agents communicate through structured protocols and challenge each other's analysis — producing intelligence that no single model could generate alone.
Agents produce intelligence — not action.
What this page covers
- The purpose of AI agents in KEK
- Core agent roles and responsibilities
- How agents coordinate through MCP
- How agent outputs feed into quantitative evaluation
- Execution and custody safety boundaries
Purpose of AI agents in KEK
KEK's agents are the reasoning core of the intelligence system:
- Agents generate structured insights and testable strategy candidates through coordinated analysis
- Each agent brings domain-specific reasoning (regime detection, narrative analysis, asset relevance, strategy synthesis)
- Cross-validation between agents catches blind spots that single-model systems miss
- Strategies must pass quantitative evaluation and paper trading before any execution is eligible
Design philosophy
KEK's agent system is built around three principles:
1) Specialization over general intelligence
Each agent is optimized for a specific domain. A regime detection agent reasons differently than a narrative agent or a strategy synthesis agent. This specialization produces deeper, more reliable analysis than a generalist model.
2) Coordination over autonomy
Agents coordinate through shared, structured interfaces. They challenge and refine each other's analysis through structured communication protocols — not by acting independently or "free-running" in production.
3) Intelligence over execution
Agent outputs are strategy intelligence — structured analysis and strategy candidates. They become eligible for execution only after quantitative evaluation and testing.
Agent architecture overview
Each agent focuses on a single domain of analysis and produces structured outputs that flow into the broader system.
Agents do not:
- Trade
- Allocate capital
- Act independently in production
Their outputs are inputs to the Quant Platform for evaluation and evolution.
Core agents
Market Regime Agent
Purpose: Identify the prevailing market regime and detect transitions.
Inputs
- Market-wide price behavior
- Volatility and dispersion metrics
- Structural and trend/range indicators
Outputs
- Regime classification (e.g., trending, ranging, volatile, transitional)
- Confidence scores and regime-shift alerts
Why it matters
This agent provides the context layer that all other agents reason against. Strategy behavior should adapt to regime — this agent tells the system which regime it's in.
Narrative Agent
Purpose: Identify macro, sector, and thematic narratives that influence asset behavior.
Inputs
- Cross-asset structure and correlation signals
- Liquidity and sentiment proxies
- Contextual drivers (risk-on / risk-off behavior, thematic rotations)
Outputs
- Narrative classifications and active themes
- Theme relevance scoring and persistence signals
Why it matters
Markets are narrative-driven. This agent detects which stories are moving capital and how persistent they are — giving the strategy agent context that pure price data cannot provide.
Asset Relevance Agent
Purpose: Rank assets most relevant to the current regime and narrative context.
Inputs
- Regime classification and confidence
- Narrative theme relevance
- Asset-level metrics (trend strength, volatility, liquidity, behavior fit)
Outputs
- Ranked asset relevance scores
- Context-aware filtering signals
Why it matters
This agent reduces noise and focuses strategy generation on assets most likely to express the targeted edge under current conditions.
Strategy Agent
Purpose: Synthesize intelligence from all agents into structured strategy candidates.
Inputs
- Regime context
- Narrative signals
- Asset relevance scores
- User-defined constraints and objectives
Outputs
- Machine-readable strategy specifications
- Parameterized rules, constraints, and assumptions
- Variant templates for evaluation
Important
These strategies are candidates for quantitative evaluation. They must pass optimization, simulation, and paper trading before any execution is possible.
Multi-agent coordination (MCP)
Agents coordinate through the MCP (Model Context Protocol), which provides a structured system for connecting models to tools, data sources, and workflows via standardized interfaces.
MCP enables:
- Shared context across agents and sessions
- Structured message passing between agent roles
- Tool coordination (data retrieval, analysis, strategy generation)
- Versioned outputs and reproducible agent artifacts
This coordination model supports coherent multi-agent behavior without relying on hidden, mutable, or implicit state. It is fundamentally different from single-model systems where one model handles all reasoning in isolation.
How agent outputs are used
Agent intelligence flows into the Quant Platform for evaluation and evolution:
Agent Coordination → Strategy Candidates → Quantitative Evaluation → Paper Trading → Monitoring & Adaptation → Optional Execution
At no point do agents bypass evaluation or execute trades.
Boundaries & safety
KEK agents are constrained by design:
- Do not access or custody user funds
- Do not store or handle private keys
- Do not self-deploy strategies
- Do not execute trades or allocate capital
All agent outputs are treated as intelligence inputs to quantitative evaluation, monitoring, and (optionally) user-authorized execution.
Why this matters
This architecture exists to:
- Prevent black-box behavior by keeping outputs structured and reviewable
- Enable deeper analysis through agent specialization and cross-validation
- Produce strategy intelligence no single model could generate alone
- Maintain long-term trust via clear responsibility boundaries
KEK treats intelligence as input to human judgment — not as execution authority.