Salvatore Attaguile

Posted on Apr 18

DCGRA: Distributed Coherence-Governed Reasoning Architecture

#ai #machinelearning #opensource #architecture

Middleware that governs multi-agent and multi-enterprise AI inference — without modifying model weights.

By Salvatore Attaguile

Independent Systems Researcher

Zenodo Preprint (v1)

https://doi.org/10.5281/zenodo.19642875

Most teams are now experimenting with multi-agent pipelines.

The models are improving rapidly, but the environments they run inside are still largely unstructured:

No clear boundaries on context
No turn-by-turn quality gate
No traceable lineage on generated artifacts

The result is predictable:

semantic drift compounds
hallucinations propagate downstream
cross-team trust breaks down
auditability becomes difficult after the fact

DCGRA is not another prompt pattern or fine-tuning wrapper.

It is a middleware governance layer that sits above any model (or mix of models) and introduces structure where many deployments still rely on improvisation.

It addresses three persistent gaps:

Context Scope — each agent reasons inside a bounded domain field
Output Evaluation — each artifact is scored before moving downstream
Artifact Lineage — validated outputs receive traceable HexID provenance

Everything else in the architecture builds on those three primitives.

The Core System Model

DCGRA is expressed as a five-tuple:

S = (F, A, C, T, P)

Where:

F — Context Field
A — Agent Reasoning Function
C — Coherence Evaluation Function
T — Domain Thresholds
P — Governance Policies

This framework does not require retraining models or changing weights.

It governs the environment inference happens inside.

Context-Bounded Field Processing (CBFP)

Each reasoning turn occurs inside a scoped domain field:

F_d = (S_d, C_d, K_d, R_d, P_d)

Where:

S_d — permissible source set
C_d — conceptual ontology / semantic anchors
K_d — grounding vector space
R_d — retrieval constraints
P_d — policy rules

If an output cannot be adequately grounded inside the active field, it does not automatically propagate.

Instead, it enters a revision cycle.

This reduces the effective hallucination surface without changing the model itself.

Coherence Score (CS)

Every output is evaluated structurally before acceptance.

CS(o_t, F_t) = w₁·SC + w₂·TS + w₃·RC + w₄·(1−UAD) + w₅·(1−CD)

Components:

SC — Sequencing Coherence
TS — Terminology Stability
RC — Relational Continuity
UAD — Unsupported Assumption Density
CD — Contradiction Density

Weights are domain configurable.

For example:

medical / legal domains can heavily weight unsupported claims
exploratory research can weight reasoning continuity more strongly

If the score falls below threshold, revision is triggered.

Turn-Level Reasoning Loop

while True:
    output = agent(field, query)
    score = CS(output, field)

    if score >= threshold:
        assign_hexid(output)
        store_and_forward(output)
        break ```
{% endraw %}



    field = revise(field, output, score)

    if max_iterations_reached:
        escalate_to_human()
        break

{% raw %}

This shifts inference from one-shot generation to governed iterative convergence.

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Multi-Agent Topology

Worker Cells

Two reasoning agents + one synthesis node.

Benefits:
• redundancy
• divergence detection
• reconciliation before propagation

Domain Grids

Worker Cells feed:
• Domain Synthesizer
• Domain Meta Node

This enables domain-level governance and routing.

Cross-Domain Cascades

Example:

MED → PHARMA → FIN → ECON

Each transition re-evaluates coherence from the receiving domain’s perspective.

Meta Agents

Used for enterprise boundary control, scoped collaboration, and policy enforcement.

⸻

HexID Artifact Addressing

Each validated artifact receives structured lineage.

Example:

MED.G3.WC7.A2.T4.V1

Encodes:
• domain
• grid level
• worker cell
• agent
• turn
• version

This enables computable provenance chains.

⸻

Why Builders Should Care

This can sit on top of existing model endpoints.

No retraining.
No weight edits.
No dependency on one vendor.

It focuses on durable infrastructure:
• structure
• evaluation
• lineage
• governance

Useful for:
• long-running agent pipelines
• enterprise AI workflows
• regulated environments
• systems where provenance matters
• cross-domain orchestration

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Core Thesis

Reliable AI systems will not come from model capability alone.

They will come from capable models operating inside environments built to hold them accountable.

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Read the Full Paper

Zenodo DOI:
https://doi.org/10.5281/zenodo.19642875

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I wrote this to be challenged, tested, and improved.

If you’re building real multi-agent systems and see gaps worth discussing, I’d like to hear them.

— Salvatore Attaguile

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