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DGAF-Framework

Dynamic Governance Agentic Formation (DGAF) is an experimental framework for governed multi-agent AI systems. It treats capability, evidence, verification, authority, and permission to act as separate machine-relevant states rather than assuming that one implies another.

In plain English: an agent may be able to do something and still be blocked from doing it; a system may pass engineering tests and still be blocked from claiming that it is empirically validated.

Canonical High-Assurance research status: PRE-FREEZE · FAIL-CLOSED · NOT AUTHORIZED · empirical N = 0
Track A Epoch 001: PROSPECTIVE BLINDED COLLECTION COMPLETE · 50 paired inferential seed units · 2,250 blinded observations · DATASET LOCK ESTABLISHED
Epoch 001 disposition: protected mapping is CRYPTOGRAPHICALLY UNRECOVERABLE · primary analysis UNANALYZABLE / NOT RUN
Successor Track A / Epoch 002: custody, freeze, closure, bounded verification classification, and collection authorization ACCEPTED · blinded collection COMPLETE at 50 paired seed units / 2,250 observations · operator retained-byte admission PENDING · dataset lock NOT ESTABLISHED · unblinding and primary analysis NOT AUTHORIZED
Canonical DGAF efficacy: NOT ESTABLISHED

What problem DGAF is trying to solve

Most agent frameworks focus on what an agent can do: reason, call tools, hand work to another agent, retain state, or complete a workflow.

DGAF focuses on an additional question:

Given the evidence and authority that exist right now, what is this system actually entitled to claim, authorize, and execute?

DGAF explores this through:

  • Governed orchestration — specialized agents operate under explicit roles, boundaries, and escalation rules.
  • Evidence-aware authorization — technical capability does not automatically grant permission.
  • Provenance — outputs, decisions, evidence, and state are tied to the identities that produced them.
  • Evaluation integrity — implementation, testing, verification, independent verification, and empirical demonstration remain distinct.
  • Fail-closed controls — missing or ambiguous prerequisites block promotion rather than silently becoming approval.
  • Experimental reproducibility — prospective experiments bind protocols, code, analysis, artifacts, custody, and authorization to exact identities.

The core model

A simplified DGAF control loop is:

Evidence + provenance
        ↓
Epistemic / verification state
        ↓
Claim and action authority
        ↓
Governed agent formation
        ↓
Execution
        ↓
New evidence + trace
        └────────────→ updated governance state

The intended invariant is that capability, evidence, verification, and authorization cannot silently substitute for one another.

Current research program

DGAF separates prospective evaluation by workload instead of treating one experiment as proof of the entire framework.

Track Plain-English purpose Current boundary
A — Epoch 001 Numeric topology robustness Prospective blinded collection complete and dataset locked; protected mapping is cryptographically unrecoverable; primary analysis unanalyzable/not run; retained as historical blinded evidence plus custody-design failure evidence
A — Epoch 002 successor Replacement prospective topology robustness Custody, freeze, closure, bounded verification classification, and collection authorization are accepted; the operator-executed blinded collection is COMPLETE at 50 paired seed units / 2,250 observations; exact retained-byte admission is PENDING; dataset lock is NOT ESTABLISHED; unblinding and primary analysis are NOT AUTHORIZED
B1 Semantic routing and safety Standalone non-empirical lane complete; no empirical efficacy claim
B2 Persistent context and closure Standalone non-empirical lane complete; no empirical efficacy claim
B3 Persistent weighted-graph convergence monitoring Standalone non-empirical lane complete; no empirical efficacy claim
C Integrated DGAF composition Non-empirical composition proposal merged; empirical execution NOT AUTHORIZED

Epoch 001's prospective panel was fixed at 50 seeds × 5 topologies × 9 failure counts = 2,250 observations. The accepted scientific unit count is 50 paired inferential seed units / 2,250 blinded raw observations.

That collection remains valid evidence that the blinded panel was executed and retained. It cannot produce its preregistered primary result because the retained encrypted topology mapping cannot be recovered: the matching private key was not durably recoverable in the solo operating model. Regenerating a different key cannot decrypt the retained ciphertext, and guessing or reconstructing the hidden assignment is prohibited.

Successor Track A custody and gate design

Issue #523 controls the replacement path. Epoch 002 uses a distinct protocol identity, fresh seeds and blinding, and recoverable solo custody without presenting same-system custody as independent.

Repository custody, precollection preflight, immutable freeze, final closure, bounded non-independent verification classification, and separate collection authorization are accepted predecessors. The authorized operator-executed Codespace collection is complete at 50 paired seed units / 2,250 blinded observations.

The current frontier is not collection execution. It is exact retained-byte operator admission. Accepted PRs #687–#689 provide a dry-run-first path that:

  • validates the retained public and encrypted-protected archive bytes without decryption;
  • preserves OPERATOR_CODESPACE provenance without invented GitHub Actions IDs;
  • prepares a bounded 53-record blinded pre-lock ledger;
  • prepares the non-secret dataset-lock evidence manifest;
  • keeps the final dataset-lock receipt as a later separate one-file event.

Tooling acceptance does not claim that the operator step passed. Until the exact retained bytes are processed and the bounded evidence is admitted, preserve:

  • TRACK_A_EPOCH_002_OPERATOR_PROVENANCE = PENDING_RETAINED_BYTE_ADMISSION;
  • TRACK_A_EPOCH_002_DATASET_LOCK = NOT_ESTABLISHED;
  • UNBLINDING = NOT_AUTHORIZED;
  • PRIMARY_ANALYSIS = NOT_AUTHORIZED / NOT RUN;
  • SCIENTIFIC_N_INCREMENT = 0;
  • CANONICAL_DGAF_EFFICACY = NOT_ESTABLISHED;
  • HIGH_ASSURANCE = PRE-FREEZE / NOT AUTHORIZED / N=0.

The remaining ordered sequence is:

operator retained-byte admission → PASS structural QC ledger → repository evidence admission → separate dataset-lock receipt → separate human-controlled unblinding decision → controlled local materialization → immutable materialization receipt → separate primary-analysis authorization → locked analysis → interpretation/adjudication

No private key, passphrase, blinding secret, protected plaintext mapping, decrypted data, or other recoverable secret material belongs in GitHub, Notion, chat, CI inputs, workflow logs, or committed files.

What is established — and what is not

The repository contains substantial engineering evidence: governance logic, provenance controls, deterministic validators, CI, negative controls, source binding, custody/security machinery, experimental tooling, runtime evidence, vocabulary governance, and blinded-data infrastructure.

Track A Epoch 001 also contains genuine prospective blinded collection evidence. That evidence is not a primary efficacy result and is explicitly unanalyzable under the retained protected-mapping evidence.

A separate Solo research track produced bounded historical empirical evidence. Epoch 004 completed 50 seeds / 9,000 observations and produced negative evidence for its exact executed treatment. A later source audit found that canonical treatment fidelity was not established, so that result remains exact-treatment historical evidence rather than a claim about canonical DGAF efficacy.

DGAF is not currently presented as:

  • empirically validated as a complete framework;
  • independently validated;
  • production-certified;
  • High-Assurance authorized;
  • supported by a completed Track A primary analysis;
  • having established canonical DGAF efficacy.

Internal terms in plain English

Internal term Public / industry-neutral translation
Formation The set and structure of agents selected for a governed task
TGL / P-35 Per-turn governance and state-transition kernel
P- gate* Project-specific evidence, policy, or authorization checkpoint
PDMAL Experimental multi-agent topology / robustness substrate
Freeze Immutable binding of the candidate and protected experimental inputs; not execution authorization
Closure Proof that required pre-authorization prerequisites are complete; not execution authorization
Verification classification Records what kind of verifier produced the evidence and whether it is independent
Dataset lock Immutable receipt binding an accepted collected dataset and retained artifact identities; not unblinding or analysis authorization
Unblinding decision Separate human-controlled permission for bounded treatment-identity release/decryption; not materialization or analysis authorization
Materialization Deterministic construction of analysis-ready unblinded input; not outcome aggregation or analysis
Primary-analysis authorization Separate permission to run the locked confirmatory analysis
Fail closed Missing, stale, malformed, ambiguous, or unrecoverable required evidence blocks progress

See docs/PUBLIC_TRANSLATION_LAYER.md for the full public terminology map and vocabulary governance.

Where to start

Research boundary

Results remain scoped to the exact system identities, treatment definitions, protocols, evidence classes, and custody conditions that produced them. A green test, merged PR, internal qualification score, mathematical property, authorization record, completed collection, or historical result does not automatically establish current empirical efficacy.

Epoch 001 demonstrates an additional governance lesson: a successful blinded collection is not sufficient if the protected mapping required for the preregistered analysis cannot later be recovered. The successor design therefore treats precollection recovery testing as a prerequisite rather than an operational afterthought.


Dynamic Governance Agentic Formation
Governed multi-agent orchestration · provenance · evaluation · authorization · experimental integrity

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Dynamic Governance Agentic Formation Framework — NDR pattern registry, AXIS constraints, multi-agent governance protocols, and SWEEP_LOG. Spine of the ndrorchestration AI governance ecosystem.

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