1. Executive Summary

The Agent Experience Index (AXI) is a benchmarking framework for evaluating the degree to which media and entertainment technology platforms are operable by autonomous software systems.

AXI does not measure whether a company has AI features.

AXI measures whether autonomous coding agents and operational agents can discover, authenticate, configure, orchestrate, monitor, govern, and economically interact with a platform through machine-operable interfaces under realistic enterprise governance constraints.

AXI is therefore an operational infrastructure benchmark, not an AI feature benchmark.

The primary audience includes:

• media technology buyers
• vendors
• product leaders
• infrastructure teams
• ecosystem partners
• analysts
• investors
• agentic workflow builders

2. Core Thesis

The next generation of enterprise software competition may increasingly depend on which platforms are most operable by autonomous systems.

Traditional enterprise software was designed around:

• human operators
• graphical user interfaces
• manual configuration
• implementation specialists
• professional services teams
• seat-based usage patterns

AXI assumes that future-ready infrastructure increasingly supports:

• machine-operable workflows
• autonomous provisioning
• programmatic governance
• software-to-software execution
• agent-mediated orchestration
• governed machine access
• usage-based or transactional economic models

The framework measures:

autonomous operability under realistic enterprise governance conditions.

3. What AXI Measures

AXI measures:

• agent-accessible documentation
• machine execution surfaces
• CLI/MCP/equivalent agent surfaces
• execution surface completeness
• autonomous provisioning capability
• governed operational accessibility
• workflow composability
• operational observability
• machine-operable governance
• commercial alignment with autonomous consumption
• public agentic-strategy commitments and roadmap signals

AXI does not measure:

• overall product quality
• customer satisfaction
• market share
• feature breadth
• generic AI sophistication
• AI marketing
• embedded copilots
• chatbot functionality

A company may have sophisticated AI features and still score poorly if its platform is not operable by autonomous systems.

A company may have little AI branding and score highly if it exposes strong machine-operable infrastructure.

4. Foundational Assessment Question

Every AXI assessment is governed by one question:

Could a modern autonomous coding agent independently implement and operate this platform using available machine interfaces and operational documentation with minimal human UI dependency?

This question governs scoring, evidence collection, and recommendations.

5. Public Discoverability vs Governed Operability

AXI does not assume that all operational interfaces should be fully public.

Enterprise systems, especially in media and entertainment, may have valid reasons to restrict access:

• rights complexity
• security concerns
• premium content protection
• financial workflows
• compliance requirements
• partner ecosystem boundaries

AXI distinguishes between:

A platform may score highly without unrestricted public exposure if it supports governed autonomous operation.

However, systems that depend on undocumented workflows, human-only onboarding, UI-only administration, or professional-services mediation should score lower.

6. Evidence Accessibility Tiers

AXI recognizes four evidence/access tiers.

Governed access is not inherently penalized.

Opaque access is penalized when it prevents reproducible assessment or autonomous operation.

Consistent Evidence Discovery

Before each assessment, AXI gathers a bounded set of relevant public and first-party evidence for the product being evaluated.

Every evaluator receives the same evidence set and the same scoring instructions. Claims must be supported by captured source material, and incomplete or conflicting results are held for review rather than published.

This consistent evidence standard makes published results comparable across products and releases while preserving an auditable record of how each assessment was produced.

7. Multi-Model Assessment Model

AXI assessments should be performed by multiple independent LLM evaluators.

Recommended evaluators:

• ChatGPT
• Gemini
• Claude

During a native scoring attempt, each evaluator receives:

• the same rubric
• the same deterministic first-party evidence set
• the same scoring instructions
• the same assessment template

Each evaluator returns:

• entity-match decision and confidence
• fired signal keys and applied penalty keys by category
• lifecycle coverage estimates where applicable
• evidence notes
• assertion-level evidence bindings for every scored signal, penalty, and positive lifecycle-coverage estimate
• recommendations

AXI centrally derives numeric category subtotals and the final score from those three structured determinations; evaluators do not supply numeric scores.

Final AXI score:

A product is assessed by all three evaluators in parallel. A score is complete and publishable only when ChatGPT, Claude, and Gemini each return exactly one valid determination for every rubric category. A 1/3 or 2/3 result is retained only as a draft partial so a later healing run can fill the missing cells without repeating successful calls.

For audit and disagreement analysis, AXI also stores a standalone score derived from each evaluator's own determinations. These three audit values are not the published consensus score. Historical evaluator rows created before standalone derivation was introduced remain labeled as legacy consolidated values rather than being retroactively reinterpreted.

Account-level provider failures are not assessment outcomes. Authentication, billing, or exhausted-quota failures halt the batch and cancel its remaining provider calls; they are recorded as infrastructure failures rather than model refusals or partial evidence. Ordinary evaluator refusals and transient errors remain eligible for the draft-partial and healing path above.

For ordinary categories, each signal and penalty is multiplied by the fraction of all three evaluators that detected it, after which the category floor and cap are applied. For lifecycle-coverage categories, the three coverage estimates are averaged and the matching rubric bucket is selected. The final AXI score is the sum of the resulting category subtotals. This consensus aggregation rewards capabilities the models agree on without silently changing the denominator when a provider fails.

The assessment, all three evaluator audit rows, category components, and consolidated recommendations are published as one transaction through the canonical publication RPC. Direct draft-to-published updates are rejected. A failure in any part leaves no partially published scorecard. Once published, evaluator rows and rubric content are immutable; a rubric change requires a new version. Provider raw_output retains the evaluator's complete structured response; the validated parsed determinations are numeric scoring authority. Public signal/penalty breakdowns and the flattened assertion-evidence index are verified from those determinations at the publication boundary. Every scored assertion must bind to an integrity-checked source in its persisted evidence packet. Recommendation source members must match recommendations in the corresponding persisted evaluator output; agreement, representative text, median impact, and ordering are then derived from the exact member multiset rather than accepted as caller labels.

Human role:

• define rubric
• define weights
• review anomalies
• improve methodology
• manage evidence quality

Humans are prevented from overriding scores directly.

8. Scores and Classifications

AXI should publish both numeric scores and classifications.

Numeric scores preserve precision and enable trend tracking.

Classifications improve legibility and sharing. The score-range → classification band table is published on the methodology page, driven by the active rubric.

9. AXI Scoring Framework

Total score: 100

Category 1 — Agent-Accessible Documentation

Objective: Can an autonomous agent learn how to operate the platform from available documentation?

Penalties:

Category 2 — Machine Execution Surfaces

Objective: Can autonomous systems execute operational tasks programmatically?

Penalties:

Category 3 — Execution Surface Completeness

Objective: How completely do CLI/MCP/API/agent surfaces cover the operational lifecycle?

This category addresses sincerity of implementation. A vendor should not receive high agentic-readiness credit for exposing a superficial CLI, toy MCP server, read-only adapter, or narrow wrapper that covers only a small portion of platform functionality.

Lifecycle areas:

Scoring:

Rules:

• Do not count endpoints mechanically.
• Assess lifecycle coverage, not command count.
• Read-only coverage is materially weaker than execution authority.
• Admin/governance coverage matters.
• Recovery and observability coverage matter.
• Thin wrappers should receive low scores.

Penalties:

Category 4 — Autonomous Provisioning and Configuration

Objective: Can autonomous systems provision, configure, and administer the platform without significant human mediation?

Penalties:

Category 5 — Commercial Agentic Alignment

Objective: Can autonomous systems economically transact with the platform?

Penalties:

Category 6 — Governance and Operational Trust

Objective: Can enterprises safely delegate operational authority to autonomous systems?

Category 7 — Agentic Strategy Signals

Objective: Has the company made public commitments toward agentic operability, even if the operational surface isn't fully built yet? This pillar captures intent — meaningful for trajectory but not for current operability. A company that publicly states an MCP roadmap is materially further along than one that's silent on it. Maximum contribution is 10/100; marketing alone caps a company at F-tier on the overall score.

Penalties: none. This pillar scores stated intent only — the absence of public commitments yields no credit rather than a deduction, and its 10-point cap prevents public messaging alone from producing a high overall score.

Documented assertion corrections

An independently reviewed discrepancy may be corrected from a retained response by the same evaluator using the same evidence packet. Only the explicitly reviewed assertions change; the remaining decisions are preserved. Such a result is labeled as derived and retains both complete structured evaluator outcomes, the exact correction mask and source provenance. It does not claim that one provider invocation authored the combined result. Scores are recalculated using the unchanged rubric and aggregation policy, without a minimum score or a requirement to agree with a previous month.

Targeted assertion corrections may request a partial response from the same evaluator against the unchanged packet. An independently reviewed exact mask can be applied to the immutable original through deterministic replay, preserving all unrelated decisions. The resulting complete assessment is explicitly derived; no full-provider re-endorsement is implied. Its explicit per-field invocation metadata preserves the original cohort rubric authority while attributing new prompt/model/usage fields to the accepted targeted invocation. Full original and partial-response provenance remain distinguishable in the exported record. Separately attributed independent corroboration may support an unchanged scored assertion without rewriting the original provider evidence.

When an accepted correction disproves a dependent recommendation's premise or estimated impact, an independent reviewer may withdraw that exact recommendation. The deletion, original content hash, assertion dependency and reason are retained as reviewer provenance; it is not attributed to the evaluator. Withdrawal supplies no replacement advice and cannot change scores. Unrelated or still-useful compound recommendations remain intact.