SHOWLABS
COPILOT + HARNESS
I led product development on a DAM/PLM-style platform and built a surface-aware multi-agent copilot on top of custom harness infrastructure. The harness layer — not the model — is what makes the product usable in a real workflow: long-conversation memory, tool-driven actions, quality-gated pipelines, and a treatment approach that holds visual consistency where one-shot generation falls apart.
CONSISTENCY
IS THE REAL PROBLEM
The hard part was not getting one impressive AI output. The hard part was consistency, controllability, and making the system behave well enough that it could sit inside a real product workflow. That applied to the copilot, the virtual studio, and the variant-generation flows.
- Batch consistency across large product sets
- Tool-driven actions inside the product
- Quality controls around generated output
SYSTEM LAYERS
Multi-Agent Copilot
Surface-aware routing, handoffs, specialized agents, and tool access so the copilot operates as part of the product rather than as a disconnected chat box.
Treatment Workflows
Shared treatment context lets the system branch a consistent creative direction across large batches of products instead of starting cold every time.
Memory + .ai Context
Long-conversation memory via compaction keeps threads usable under their own history. An internal .ai context system makes coding agents more consistent across the codebase.
Quality-Gated Variants
Variant generation, post-processing, confidence scoring, and approval flows turn generated assets into something the product can actually use.
SUPPORTING
SYSTEMS
The interesting part of this work was not any one model or any one AI feature. It was the surrounding workflow system: context, orchestration, controls, review, and the product logic that made the output usable.
Surface-Aware Context
The system gathers the right product, asset, route, and workflow context before the model does anything useful.
Agent Orchestration
Specialized agents, tools, and workflow controls handle different parts of the task instead of forcing everything through one chat surface.
Quality Gates
Generated outputs go through scoring, review, and product-specific checks before they are promoted into the workflow.
Real Product Use
The point is not a demo. The point is making AI outputs usable inside a real product with real users and real constraints.