Most teams are optimizing prompts.

Very few are optimizing how AI systems interact with each other.

In practice, different models reason differently. And when you let them challenge each other, the quality of your outcomes improves significantly.

Here’s a simple example from a real production issue.

The problem — duplicate entities in a knowledge graph

A bug surfaced where duplicate organizations were being created:

Same entity
Different casing (e.g., “grow” vs “Grow”)
Failed deduplication during ingestion

A straightforward issue — but a good test for AI reasoning.

Step 1 — Run parallel investigations

Two separate models were given the same prompt:

ChatGPT
Claude Opus

Each was asked to:

Investigate the root cause
Propose a fix
Explain their reasoning

Step 2 — Cross-validate the outputs

Instead of accepting one answer:

The output from one model was fed into the other
The second model critiqued the diagnosis
Differences were highlighted explicitly

The result:

One model identified a key detail the other missed
It recommended adopting the alternative fix

Step 3 — Select and execute the best solution

The final approach:

Combined insights from both models
Used the stronger implementation plan
Leveraged the model with higher token capacity for execution

Why this works

Different models:

Have different training biases
Emphasize different edge cases
Approach reasoning in distinct ways

By orchestrating them together, you:

Reduce blind spots
Increase confidence in decisions
Improve production outcomes

The bigger insight

This isn’t just about debugging.

It’s a preview of how AI systems will operate:

Not as single assistants
But as collaborative agents with competing perspectives

The advantage won’t come from using AI.

It will come from how you orchestrate it.

Talk to us

Want to go deeper into agentic workflows and AI orchestration? Watch the full demo above, then reach out to explore how to apply this inside your engineering stack.