Round 100

Event Sourcing Adoption for Order Platform

A mid-sized e-commerce company is rebuilding its order management system to handle 10x projected growth and support new features like order timeline replay, dispute resolution, and real-time inventory sync across multiple fulfillment centers. The engineering leadership must decide how order state changes are captured and persisted. Key goals: strong auditability for disputes and compliance, ability to reconstruct historical state, low operational complexity for a team of 12 engineers, and support for near-real-time downstream consumers (billing, shipping, analytics). Constraints: the team has limited prior experience with event-sourced systems, existing reporting tools expect relational query access, and the system must stay operable during a phased migration from the legacy monolith over 12-18 months. Tradeoffs include storage and query complexity, debugging difficulty, consistency guarantees, team learning curve, and how much rework is needed if the choice proves wrong later.

Dashboard

Status

DECIDED

Winner

D · Apply event sourcing selectively only to the most audit-critical aggregates (orders and payments), while using conventional CRUD for lower-risk entities like customer profiles.

Majority

D · Apply event sourcing selectively only to the most audit-critical aggregates (orders and payments), while using conventional CRUD for lower-risk entities like customer profiles.

Duration

6023 ms

Topic

Options

D Apply event sourcing selectively only to the most audit-critical aggregates (orders and payments), while using conventional CRUD for lower-risk entities like customer profiles.
2.36 of 3 PPV winner
B Keep the relational database as-is and implement the outbox pattern with change data capture (CDC) to stream state changes as events, avoiding event sourcing altogether.
0.64 of 3
A Use CQRS with a traditional relational database as the source of truth, maintaining separate optimized read models and publishing domain events for synchronization.
0.00 of 3
C Stick with traditional CRUD on a relational database, supplemented by a detailed audit log table that records every state transition for compliance and history.
0.00 of 3
E Adopt full event sourcing: the event store becomes the single source of truth for all order-related aggregates, with projections built for every read need.
0.00 of 3

Checks

Baselines

PPV no gold answer
Majority no gold answer
Comparison PPV matched majority
k samples8
Agents3
Parsed samples 24 / 24
Quality near unanimous split agent disagreement

PPV

Top Influences

GPT 5.4 mini · delegate 1.182
Gemini 3 Flash Preview · delegate 1.182
Claude Sonnet 5 · delegate 1.000

Samples

Answer Distribution

Claude Sonnet 5 B: 4 D: 4
GPT 5.4 mini D: 8
Gemini 3 Flash Preview D: 8

PPV

Influences

Label Role Score
GPT 5.4 mini delegate 1.182
Gemini 3 Flash Preview delegate 1.182
Claude Sonnet 5 delegate 1.000

Voting matrix

Constructed Delegation Matrix

To / From Claude Sonnet 5 GPT 5.4 mini Gemini 3 Flash Preview
Claude Sonnet 5 0.0000.0000.000
GPT 5.4 mini 0.1820.0000.000
Gemini 3 Flash Preview 0.1820.0000.000
A 0.0000.0000.000
B 0.6370.0000.000
C 0.0000.0000.000
D 0.0001.0001.000
E 0.0000.0000.000

Embeddings

Sample Embeddings

24 samples
Claude Sonnet 5 sample #0 · D · 1536 dims D Claude Sonnet 5 sample #1 · B · 1536 dims B Claude Sonnet 5 sample #2 · D · 1536 dims D Claude Sonnet 5 sample #3 · D · 1536 dims D Claude Sonnet 5 sample #4 · B · 1536 dims B Claude Sonnet 5 sample #5 · B · 1536 dims B Claude Sonnet 5 sample #6 · D · 1536 dims D Claude Sonnet 5 sample #7 · B · 1536 dims B GPT 5.4 mini sample #0 · D · 1536 dims D GPT 5.4 mini sample #1 · D · 1536 dims D GPT 5.4 mini sample #2 · D · 1536 dims D GPT 5.4 mini sample #3 · D · 1536 dims D GPT 5.4 mini sample #4 · D · 1536 dims D GPT 5.4 mini sample #5 · D · 1536 dims D GPT 5.4 mini sample #6 · D · 1536 dims D GPT 5.4 mini sample #7 · D · 1536 dims D Gemini 3 Flash Preview sample #0 · D · 1536 dims D Gemini 3 Flash Preview sample #1 · D · 1536 dims D Gemini 3 Flash Preview sample #2 · D · 1536 dims D Gemini 3 Flash Preview sample #3 · D · 1536 dims D Gemini 3 Flash Preview sample #4 · D · 1536 dims D Gemini 3 Flash Preview sample #5 · D · 1536 dims D Gemini 3 Flash Preview sample #6 · D · 1536 dims D Gemini 3 Flash Preview sample #7 · D · 1536 dims D
Claude Sonnet 5 GPT 5.4 mini Gemini 3 Flash Preview

Samples

Agent Responses

Claude Sonnet 5 openai_compatible · anthropic/claude-sonnet-5
#0 D · Apply event sourcing selectively only to the most audit-critical aggregates (orders and payments), while using conventional CRUD for lower-risk entities like customer profiles. 1536 dims

Given the team's limited event-sourcing experience, the need to preserve relational query access for existing reporting tools, and the requirement to stay operable during a 12-18 month phased migration, a full event-sourcing rewrite (E) or applying it broadly ...

#1 B · Keep the relational database as-is and implement the outbox pattern with change data capture (CDC) to stream state changes as events, avoiding event sourcing altogether. 1536 dims

CDC/outbox with the relational DB unchanged preserves existing relational query access for reporting, minimizes learning curve and operational complexity for a team new to event sourcing, and provides near-real-time events for downstream consumers while keepin...

#2 D · Apply event sourcing selectively only to the most audit-critical aggregates (orders and payments), while using conventional CRUD for lower-risk entities like customer profiles. 1536 dims

Given the team's limited event-sourcing experience, the need for relational query support for existing reporting tools, and the requirement to stay operable during a 12-18 month phased migration, a selective approach minimizes risk while still delivering stron...

#3 D · Apply event sourcing selectively only to the most audit-critical aggregates (orders and payments), while using conventional CRUD for lower-risk entities like customer profiles. 1536 dims

Given the team's limited event-sourcing experience, the need for relational reporting access, and a phased 12-18 month migration, full event sourcing (E) risks excessive complexity and operational strain, while pure CRUD+audit log (C) or CDC-only (B) fail to d...

#4 B · Keep the relational database as-is and implement the outbox pattern with change data capture (CDC) to stream state changes as events, avoiding event sourcing altogether. 1536 dims

Given the team’s limited event-sourcing experience, existing relational reporting needs, and the constraint of maintaining operability during a 12-18 month phased migration, a full event-sourcing rewrite (E) or applying it broadly (D) would introduce excessive...

#5 B · Keep the relational database as-is and implement the outbox pattern with change data capture (CDC) to stream state changes as events, avoiding event sourcing altogether. 1536 dims

Given the team's limited event-sourcing experience, the need for relational reporting compatibility, and a phased 12-18 month migration, full event sourcing (E) or selective ES (D) risk too much learning curve and rework complexity too early. Option B (outbox ...

#6 D · Apply event sourcing selectively only to the most audit-critical aggregates (orders and payments), while using conventional CRUD for lower-risk entities like customer profiles. 1536 dims

Given the team's limited event-sourcing experience, existing relational reporting needs, and phased migration constraints, a full event-sourcing approach (E) or scattering it across all aggregates risks excessive complexity and rework. Option D targets event s...

#7 B · Keep the relational database as-is and implement the outbox pattern with change data capture (CDC) to stream state changes as events, avoiding event sourcing altogether. 1536 dims

Given the team's limited event-sourcing experience, existing relational reporting needs, and a phased 12-18 month migration, full event sourcing (E) or even selective ES (D) risks too much complexity and rework too soon. Option B (outbox+CDC) gives near-real-t...

GPT 5.4 mini openai_compatible · openai/gpt-5.4-mini
#0 D · Apply event sourcing selectively only to the most audit-critical aggregates (orders and payments), while using conventional CRUD for lower-risk entities like customer profiles. 1536 dims

The strongest fit is the option that gives auditability, historical reconstruction, and real-time downstream updates without forcing the team into full event-sourcing complexity across the entire domain. Full event sourcing would improve replay and auditabilit...

#1 D · Apply event sourcing selectively only to the most audit-critical aggregates (orders and payments), while using conventional CRUD for lower-risk entities like customer profiles. 1536 dims

Event sourcing best fits the need for full auditability, historical reconstruction, and replayable order timelines, especially for disputes and downstream sync. However, the team’s limited experience, operational simplicity goals, legacy reporting needs, and p...

#2 D · Apply event sourcing selectively only to the most audit-critical aggregates (orders and payments), while using conventional CRUD for lower-risk entities like customer profiles. 1536 dims

The requirements strongly favor strong auditability, replayable history, and near-real-time downstream consumers, which are all natural fits for event sourcing. However, the team has limited experience, needs low operational complexity, and must support a phas...

#3 D · Apply event sourcing selectively only to the most audit-critical aggregates (orders and payments), while using conventional CRUD for lower-risk entities like customer profiles. 1536 dims

The system’s strongest needs are auditability, replay, and reconstructing historical state, which are all natural fits for event sourcing. But the team has limited experience, needs relational access for reporting, and must migrate gradually with low operation...

#4 D · Apply event sourcing selectively only to the most audit-critical aggregates (orders and payments), while using conventional CRUD for lower-risk entities like customer profiles. 1536 dims

Event sourcing fits the goals of strong auditability, timeline replay, and reconstructing historical state, but doing it everywhere would add significant operational and learning complexity for a 12-person team during a long migration. A selective approach lim...

#5 D · Apply event sourcing selectively only to the most audit-critical aggregates (orders and payments), while using conventional CRUD for lower-risk entities like customer profiles. 1536 dims

Event sourcing best fits the need for strong auditability, historical reconstruction, and replayable order timelines, while also supporting downstream consumers through projections. However, full adoption would add significant learning, debugging, and migratio...

#6 D · Apply event sourcing selectively only to the most audit-critical aggregates (orders and payments), while using conventional CRUD for lower-risk entities like customer profiles. 1536 dims

The company’s top needs are auditability, historical reconstruction, and near-real-time downstream consumers, which are all strong fits for event sourcing. However, the team has limited experience, needs relational reporting during a long migration, and must k...

#7 D · Apply event sourcing selectively only to the most audit-critical aggregates (orders and payments), while using conventional CRUD for lower-risk entities like customer profiles. 1536 dims

The requirements strongly favor strong auditability, replayable history, and real-time downstream consumers, which are all natural fits for event sourcing. However, the team has limited experience, existing reporting expects relational access, and the migratio...

Gemini 3 Flash Preview openai_compatible · google/gemini-3-flash-preview
#0 D · Apply event sourcing selectively only to the most audit-critical aggregates (orders and payments), while using conventional CRUD for lower-risk entities like customer profiles. 1536 dims

The decision requires balancing the need for strong auditability and historical state reconstruction with the constraints of team experience and operational simplicity. Option (D) minimizes risk by applying event sourcing only where its benefits (timeline repl...

#1 D · Apply event sourcing selectively only to the most audit-critical aggregates (orders and payments), while using conventional CRUD for lower-risk entities like customer profiles. 1536 dims

To achieve the goals of historical state reconstruction and order timeline replay while managing a team's limited experience and tight constraints, selective event sourcing is the most balanced approach. Option (D) allows the team to gain critical auditability...

#2 D · Apply event sourcing selectively only to the most audit-critical aggregates (orders and payments), while using conventional CRUD for lower-risk entities like customer profiles. 1536 dims

Event sourcing the entire system presents a high risk due to the team's limited experience and the need for relational query access. Option D balances the requirements by applying event sourcing only where high auditability and timeline replay are critical (or...

#3 D · Apply event sourcing selectively only to the most audit-critical aggregates (orders and payments), while using conventional CRUD for lower-risk entities like customer profiles. 1536 dims

To achieve the goals of historical reconstruction and order timeline replay while managing a steep learning curve and legacy integration, a targeted approach is most effective. Option (D) balances the need for rigorous auditability and state reconstruction for...

#4 D · Apply event sourcing selectively only to the most audit-critical aggregates (orders and payments), while using conventional CRUD for lower-risk entities like customer profiles. 1536 dims

Option (D) balances the complex requirements of auditability and timeline replay with the team's limited experience and the need for a phased migration. By applying event sourcing selectively to critical domains like orders and payments, the company meets its ...

#5 D · Apply event sourcing selectively only to the most audit-critical aggregates (orders and payments), while using conventional CRUD for lower-risk entities like customer profiles. 1536 dims

To address the goals of auditability and state reconstruction while managing the team's limited experience and the need for relational reporting, a pragmatic, hybrid approach is most suitable. Option (D) allows the team to leverage the benefits of event sourci...

#6 D · Apply event sourcing selectively only to the most audit-critical aggregates (orders and payments), while using conventional CRUD for lower-risk entities like customer profiles. 1536 dims

To address the requirements of auditability and state reconstruction while managing the team's limited experience and the need for relational reporting, a pragmatic approach is necessary. **Option (D)** provides the benefits of event sourcing (timeline replay,...

#7 D · Apply event sourcing selectively only to the most audit-critical aggregates (orders and payments), while using conventional CRUD for lower-risk entities like customer profiles. 1536 dims

Event sourcing for all aggregates (Option E) poses too high a learning curve and operational risk for a small team with limited experience, while pure CRUD (Option C) fails the requirement for advanced timeline replay. Option D balances these by applying event...

Timings

Stages

Sampling5335 ms
Embedding678 ms
Signals10 ms
PPV0 ms

Errors

Agent and Round Errors

No errors recorded.