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3 Important Lessons For Creating Production Grade Agents
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
FIND US
YouTube - https://www.youtube.com/@PandCpodcast
Bluesky - https://bsky.app/profile/pandcpodcast.bsky.social
X - https://x.com/_pandcpodcast
Instagram - https://www.instagram.com/_pandcpodcast
LinkedIn - https://www.linkedin.com/company/p-and-c-podcast
View all episodes
By David Vuong and Ilan RotenbergWondering 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
FIND US
YouTube - https://www.youtube.com/@PandCpodcast
Bluesky - https://bsky.app/profile/pandcpodcast.bsky.social
X - https://x.com/_pandcpodcast
Instagram - https://www.instagram.com/_pandcpodcast
LinkedIn - https://www.linkedin.com/company/p-and-c-podcast