BNY · Enterprise Agent Platform Eliza

A Evidence level

BNY: Enterprise Agent Platform Eliza

Publisher: OpenAI · Vendors ' official customer information · Direct sources at the case level

Claim origin: Public disclosure by manufacturer or customer · Independent verification: No · Accessed: 2026-09-19

Evidence level measures whether a source can be located and reviewed; it does not mean vendor-reported claims were independently audited.

Standard case | Useful reference with remaining gaps 9 / 12

Business context1 / 2

Transformation workflow1 / 2

Technical workflow2 / 2

Human roles & governance1 / 2

Measured outcomes2 / 2

Source traceability2 / 2

Remaining gaps: Business context, Transformation workflow, Human roles & governance

The focus in this case is on transforming the Enterprise Agent Platform Eliza from a one-time analysis or single-point tool to a production stream that can be embedded into real business, verifiable and sustainable.

Business problem

Large financial institutions need to allow large numbers of employees to build Agent themselves under security governance, rather than being developed by central teams on a case-by-case basis.

Solution

BNY established AI Hub and internal platform Eliza to access the OpenAI model and corporate governance to allow staff to develop controlled Agent and workflows on the platform.

Technical architecture & production workflow

Step 01

Enterprise knowledge/documents/data/business systems

→

Step 02

ChatGPT Enterprise/Work access context within permission

→

Step 03

Staff member or custom GPT/Agent completes analysis, generation or automation

→

Step 04

Connector/rules/business governance control data and action boundaries

→

Step 05

Staff review of key outputs and implementation

→

Step 06

HF process sedimentation as template, GPS or Agent and continuous assessment

Key technology & infrastructure components

OpenAI APIChatGPTInternal Agent Platform ElizaBusiness governanceCompetences/audits

Human roles & accountability

Staff are responsible for operational judgement; central AI/Ret Team defines governance and platform boundaries.

FDE delivery actions

Select high-frequency and measurable production streams with financial and corporate knowledge teams, rather than simply deploying chat portals
Identifying the business context, system privileges, tools and security boundaries required for the model
Connect models to real software/data/business processes and establish tests, Evals or validation of certainty
Design the Human-in-the-lop and failed upgrade paths to ensure clear boundaries of responsibility
Continuous succession of Prompt, context, tools and processes based on production usage, errors and user feedback

Reusable delivery patterns

Enterprise AI effects depend on context, tools, validation and adoption rates, and not only on model capabilities
Quantifiable workflows first followed by product/platformization of successful models
As far as you can, Agent has access to tests, CI/CDs and security checks to form a closed loop.
High-risk industries must leave professional responsibilities and establish sustainable Evals

Business outcomes & delivery results

Officially, the platform supports 125+ online usage, 20,000 employees are willing to build Agent, and legal review time has been reduced by 75 per cent.

Evidence boundaries & verification notes

The official case of OpenAI clearly revealed a decline in the Eliza platform, the 20k builder, the 125+ example and 75% of the legal review time.
The source can be directly located in the case; the value remains disclosed by the source and does not represent an independent audit.

Primary source: BNY: Enterprise Agent Platform Eliza ↗