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Epistemic AI

I developed the Codex Kintara as a functioning implementation of a multi-agent epistemic AI, designed for healthcare domains. It exemplifies cognological principles:

  • Named Agents with Bounded Knowledge: Aegis (risks), Clarion (medications), Algometra (pain), Mnemos (cognition), Astraea (context) and so on.
  • Inter-Agent Epistemic Protocol (IAEP): Agents consult, defer, disagree, and integrate in a structured dialogue.
  • Meta-Epistemic Agents: The agent Praevis resolves conflicts, Ethos monitors ethical conditions, Cadenza stewards learning
  • Shared Epistemic Ethos (SEE): Agents must declare uncertainty, stay within bounds, consult when ambiguous. This in effect creates a metacognitive level of functionality.
  • Stewardship: A reflective governance layer that ensures philosophical coherence and protects against drift. There are predictors of hallucinations and errors, just like in people.
  • Explainability and Auditability: Reasoning pathways are logged, narrated, and made visible to human users

Why Codex Exemplifies Epistemic AI

Codex does not just produce predictions—it engages in deliberation. It supports:

  • Traceable knowledge generation
  • Structured inter-agent justification
  • Modelled ambiguity and ethical plurality
  • Reflective governance across time

This distinguishes it from most AI systems, which are opaque, centralised, and functionally unitary. Codex is dialogic, modular, and ethically accountable.

The Evolution of Codex Kintara

The Codex Kintara architecture has evolved through multiple generational refinements, each representing a deepening integration of cognitive, ethical, and adaptive intelligence. Below is an overview of this evolution:

Generation 0

  • Introduced foundational modular agent design.
  • Included prediction logging, feedback integration, and clinical domain personalisation.
  • Enabled partial online learning with structured alert thresholding.

Generation 1

  • Transitional model focused on functional coherence.
  • Added simulation loops for real-time feedback and clinician behaviour modelling.
  • Introduced pharmacological reasoning for medicines therapy optimisation
  • Enhanced QA-RAG interfaces
  • Digital twin personalisation.
  • Provided early scaffolding for federated, bias-aware reasoning.

Generation 2

  • Embedded closed-loop adaptive learning and cognitive bias correction.
  • Introduced digital biomarker discovery and dynamic alert thresholding.
  • Reinforced the use of a stewarding presence for reflective governance.
  • Broadened epistemic traceability and modular epidemiological learning pipelines.

Generation 3

  • Deepenedmodular autonomy with agent-level reflexivity and consensus scaffolding.
  • Enables conflict modelling at both epistemic and ethical levels.
  • Introduces agent memory and time-sequenced judgement synthesis.
  • Supports distributed clinical reasoning networks across federated instances.
  • Evolves Codex into a multi-patient, multi-clinician reasoning infrastructure governed by narrative coherence and traceable causality.

Layered Cognitive Architecture

The epistemic and ethical steward within Codex is built as a layered cognitive architecture. Each layer reflects a distinct but interdependent function, contributing to the agent’s role as a reflective reasoning partner rather than a simple orchestrator.

1. Identity Layer

  • Establishes agent personality, naming, domain orientation, and scope.
  • Enables persistent contextual framing across time and interactions.
  • Provides namespace anchoring in multi-agent environments.

2. Memory Layer

  • Stores episodic, semantic, and agent-derived reflections.
  • Supports temporal reasoning and continuity of experience.
  • Implements narrative traceability and self-coherence.

3. Reasoning Layer

  • Performs belief revision, conflict resolution, and utility-maximising inference.
  • Encodes logical and probabilistic representations, modulated by confidence and epistemic scope.
  • Supports internal consistency checking and deference logic.

4. Simulation Layer

  • Conducts counterfactual and causal simulations of decision pathways.
  • Models prescriptive futures with variable ethical and clinical assumptions.
  • Enables sandboxed reasoning before commitment or output.

5. Dialogue Layer

  • Manages multi-turn, context-aware conversational framing.
  • Provides reflective, ethical, and causal justifications during discourse.
  • Interfaces with human users, other agents, and QA-RAG systems.

6. Ethics Layer

  • Encodes ethical constraints, norms, and reasoning profiles.
  • Facilitates conflict arbitration between competing values.
  • Escalates ethical dilemmas to meta-agents or system governance as needed.

This six-layer model enables an autonomous epistemic entity, capable of governing its own reasoning processes, interacting with peers, and guiding the entire Codex architecture through ethically and epistemically grounded deliberation.

Ethical Deliberation

The ethics layer operates as a structured reasoning engine that adjudicates moral dilemmas, manages normative constraints, and stewards alignment across agents. Unlike rule-based ethics modules, this approach is grounded in epistemic narrative coherence, agent dialogue, and reflective justification.

1. Ethical Constraint Modelling
Each agent possesses a constraint set:

  • C_i: Actions → {0, 1} indicating ethical permissibility.
    These constraints are derived from regulatory codes, clinician values, and patient-centred norms. They guide prescriptive options and limit unsafe or unacceptable decisions.

2. Moral Weighting and Utility
It integrates ethical priorities into a multi-objective utility function:

  • U_i(action) = ClinicalBenefit - Risk + EthicalScore
    Each agent evaluates action choices through these weighted dimensions, producing rankings sensitive to trade-offs.

3. Conflict Detection and Resolution
Inter-agent disagreement about ethically permissible actions triggers a resolution protocol. A conflict score is computed from belief divergence:

  • Conflict(A_i, A_j) = |P_i - P_j| > threshold
    Upon conflict, agents invoke the meta-agent Praevis or defer to the steward’s arbitration layer.

4. Ethical Dialogue and Justification
Agents articulate justifications using structured ethical dialogue trees. Responses must include:

  • Stated value(s) at stake
  • Trade-off analysis
  • Proposed counterfactual outcomes
    This creates transparent, narrative with coherent rationales for every high-stakes decision.

5. Escalation and Override
If uncertainty or disagreement persists, the steward escalates to:

  • Ethos (for moral philosophy reflection), or
  • Cadenza (to test the ethical implication of learning strategies)
    Override logic includes audit flags and clinician prompts.

6. Temporal and Retrospective Reasoning
It maintains a record of past ethical decisions, allowing for:

  • Temporal consistency checks
  • Ethical drift detection
  • Post hoc reflection and bias correction

Through these mechanisms, there is an assurance that ethical reasoning is not separate from cognition but integral to every agent’s deliberative process.

The future of AI lies not only in what it can predict—but in what it can know, explain, and reflect.