Wondering how to take your proof of concept agent to production? Ilan walks through three key lessons from building a competitive analysis agent in n8n, including tips for optimizing your prompts, how to break work into sub-agents, analyze logs, and rethink your workflow design.

CHAPTERS

00:00 - Intro and Episode Overview

01:31 - Sponsor: Querio

01:59 - The Competitive Analysis Agent Problem

04:29 - Lesson 1: Use Observability and Logs to Debug

06:43 - Hitting Token Limits and Iteration Caps

09:05 - Lesson 2: Split Into Sub-Agents When Needed

12:40 - The Parallel Processing Problem in n8n

14:11 - Lesson 3: Sequential Design When Tools Don't Run Parallel

16:29 - Wrapping Up and Key Takeaways

KEY LESSONS

Lesson 1: Use observability and logs to debug agent performance. When the market research agent missed a $100M Series C raise, analyzing n8n logs revealed superficial Perplexity requests, leading to prompt optimization techniques like specifying exact information types (official news, product updates, partnerships, industry coverage) instead of letting the agent decide what to search for.

Lesson 2: Split into sub-agents when hitting limits or performance issues. Token limits (10,000 per second on Anthropic) and iteration caps (10 default in n8n) mean one agent doing too many jobs leads to context growth and hallucination risk. Breaking into focused sub-agents (news researcher, sentiment analysis) with specific prompts solves this.

Lesson 3: Design for sequential execution when tools don't support parallel processing. n8n runs left to right, top to bottom, so forcing a parallel-looking workflow creates unpredictable execution order. Accepting sequential design and removing deterministic steps (like pulling competitor lists from Google Sheets) from agent control improves reliability.

SPONSORS

Querio → querio.ai
n8n → n8n.io

LINKS

Download the n8n workflow here
n8n - https://n8n.io

Perplexity - https://www.perplexity.ai

Product Compass (Pawel Huryn) AI Agent Workshop

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