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Step 6: Test & Deploy

Step 6: Test & Deploy

Full Pipeline Test

Start the dev server:

Terminal window
kernova dev

Open http://localhost:3000/chat and try the full pipeline:

Input: “Write a blog post about how AI is changing software development in 2026”

Expected behavior:

  1. You see ”🔍 Starting research…”
  2. Wait 5-10 seconds (Researcher is searching the web)
  3. You see ”🧠 Analyzing findings…”
  4. Wait 3-5 seconds (Analyzer is processing)
  5. You see “✍️ Writing content…”
  6. Wait 5-10 seconds (Writer is composing)
  7. Final article appears — well-structured, sourced, engaging

Total time: 15-30 seconds for a complete researched article.

Troubleshooting

ProblemCauseFix
No web resultsDuckDuckGo rate limitedAdd TAVILY_API_KEY to .env.local
Very slowThree sequential API callsNormal — each takes 5-10s
Router shows help for everythingKeywords don’t matchUse trigger words: “write”, “research”, “analyze”
Empty articleResearcher found nothingTry a broader topic

Deploy

Terminal window
git init && git add -A && git commit -m "Content pipeline - multi-agent"
git remote add origin https://github.com/YOU/content-pipeline.git
git push -u origin main
kernova login --key kn_live_xxx
kernova deploy

What You Built

✅ 4 specialized agents collaborating
✅ Router pattern for intelligent request routing
✅ Sequential pipeline (research → analyze → write)
✅ Delegation between agents
✅ Progress indicators for long-running operations
✅ Web search integration for real-time data
✅ Deployed to production

Key Takeaways

  1. Split by skill — Each agent does one thing well
  2. Code routing is fast — Use handlers for deterministic decisions
  3. Temperature matters — 0.3 for facts, 0.8 for creative
  4. Delegation is powerful — ctx.delegate() connects any agents
  5. Progress updates — ctx.emit() keeps users informed

Ideas to Extend

  • Add a reviewer agent that fact-checks the writer’s output
  • Add a scheduler that auto-generates daily content
  • Add different output formats (social thread, newsletter, video script)
  • Add a feedback loop — user rates the article, agents improve
  • Connect to CMS — auto-publish to your blog via a tool

Next tutorial: Autonomous DevOps Agent → (advanced)