infragistics

@infragistics

Infragistics helps product, engineering & business teams build AI-ready apps—from agent-assisted UI to embedded analytics & data-driven workflows.

Cranbury, New Jersey

infragistics.com

Joined September 2008

infragistics
@infragistics
15h
AI-generated UI can compile and still be costly to own. Old API. A component rebuilt from scratch. More code to maintain. Without current product context, speed becomes rework. Give the agent real docs and APIs before it writes. More: infragistics.com/products/ignit…
infragistics
@infragistics
Aug 14
New Rails AI agents benchmark. 8 models. 21 real tasks. Biggest separator: did the agent use the actual Rails APIs or hand-roll its own version? Most of the time they reinvent it. That code becomes yours to maintain. Better context changes how the agent codes. Same pattern
infragistics
@infragistics
Aug 12
We fixed a bug in the template. Generated again, clean. Reverted the fix. Same model. Same prompts. Fresh session. The bug was still gone. The model never learned. The surrounding system did: Skills, workspace context, local caches. AI results improve over time for a reason
infragistics
@infragistics
Aug 11
The import compiles. The props are from a version that no longer exists. So we pointed Claude Code at the current source: two Ignite UI MCP servers in a React project. Type /mcp. Both live. It reads the current API before it writes. 🔗 How the Ignite UI CLI and MCP server give
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infragistics
@infragistics
Aug 11
MCP vs RAG for documentation agents: 100 matched queries. MCP won 52% of head-to-heads (vs 38% for RAG), especially on exact API lookups. Trade-off: ~2.5× slower and much more expensive. Use MCP when correctness matters more than speed. Keep RAG for high-volume work. Full
MCP vs RAG: 100-Query Benchmark Reveals the Costly Winner
From infragistics.com