Co-built
Execution Engine
Prompt-Driven Execution Engine · Ascendion
- 1.5h → 15m
- workflow planning time
- 65% → 85%
- first-pass acceptance
- 1.5K+
- users
Problem
Turning a high-level prompt into a structured product artifact meant long manual planning cycles, and the first draft often missed the mark — workflow planning took around 1.5 hours and only a fraction of outputs were accepted on the first pass. That made artifact generation slow and unreliable to scale across teams.
Approach
I co-built a prompt-driven execution engine using LLM-based agent orchestration with RAG and ReAct pipelines. The orchestration drives planning from a prompt, RAG grounds outputs in relevant context, and the ReAct loop reasons and acts toward a structured product artifact — automating generation end to end.
Impact
- Compressed workflow planning from 1.5 hours to 15 minutes.
- Raised first-pass acceptance from 65% to 85%.
- Enabled automated generation of structured product artifacts for 1.5K+ users.
Contribution: Co-built — a collaborative effort I contributed to substantially.
// constraints
Outputs had to be deterministically structured — free-form LLM responses weren't acceptable as product artifacts. The engine needed to enforce schema on outputs while remaining flexible enough to handle diverse prompt inputs across teams.
// tradeoffs
Paired RAG for context grounding with a ReAct reasoning loop for step-by-step artifact construction: more complex than a single-pass generation approach, but the only way to achieve the 85% first-pass acceptance rate. The added latency per request was acceptable given the 6× planning time reduction.