TL;DR ⏩
- Problem Manual community research → synthesis → first draft = ~3 h.
- Solution Four collaborating agents powered by VoltAgent, Composio, and OpenAI GPT‑4o.
- Result Insight report + Markdown draft in 6 min 12 s (≈ 80% faster).
- Stack
- Node 20
- VoltAgent
- Composio MCP
- OpenAI
- Benefit Not replacing human content creators, but supercharging your workflow by automating the tedious research phase.
If that's enough to get you excited, grab the repo and run. Otherwise, let me walk you through the story behind the numbers and why this matters for both technical teams and marketing professionals.👇
From half‑day Reddit deep‑dives to a draft in 6 minutes, all orchestrated in TypeScript.
1 – A Marketer's Confession: The Research & First-Draft Bottleneck
As someone constantly exploring new tech, I'm always hunting for ways to transform mind-numbing chores into streamlined processes. That thought hit me again after losing another afternoon spelunking through r/MachineLearning threads and Hacker News comment chains.
If you've ever tried to create insightful content, you know the workflow:
- Dive into communities to understand what your audience is actually discussing
- Manually trawl through posts and comments for patterns
- Synthesize those findings into key themes and insights
- Finally begin writing your first draft
This initial phase can easily consume 3+ hours, if not days. The research + blank-page combo is lethal: tabs multiply, energy drains, and suddenly the evening is gone.
So I asked myself:
What if the research dungeon had its own NPCs — tiny AI coworkers that gather clues, compare notes, and return with usable intel?
That's when the AI Content Strategist experiment was born.
2 – Meet the Mini‑Team 🧑💻🧑🔬🧑🏫🧑🎨
I structured my experiment around a multi-agent system, where each AI plays a specialized role:
| Agent (Persona) | Super‑power | Runs on | One‑Sentence Prompt |
|---|---|---|---|
| Content Strategist (Project Manager) | Breaks the job into tasks, delegates, double‑checks outputs. | gpt‑4o | "Plan ➜ think ➜ delegate ➜ review ➜ ship." |
| Data Scout (Intern Reporter) | Uses Composio tools to scrape posts & comments from Reddit/HN. | gpt‑4o‑mini | "Return JSON arrays of raw content for {topic}." |
| Insight Digger (Analyst) | Clusters pain points, unmet needs, wild ideas. | gpt‑4o | "Produce Markdown bullets: Pain Points / Needs / Opportunities." |
| Draft Generator (Copywriter) | Turns insights into a human‑readable article. | gpt‑4o | "Write a blog post for PM/PMM readers. Tone: friendly, practical." |
Think of them as an assembly line: raw discussion threads go in one end; organized insights and paragraph-formatted prose come out the other.
It's important to note that the goal wasn't to create a fully polished, publish-ready article with AI, but to slash the time spent on initial research and get a solid first draft that a human writer can then refine.
3 – Under the Hood (Narrated Walkthrough)
The whole orchestra is about 150 LoC. The heart is the ContentStrategySupervisorAgent; its prompt reads like a mini‑SOP:
- Think — break the topic into search terms and target communities.
- Delegate — ask Data Scout to fetch everything.
- Analyze — decide if the haul is good enough or loop back.
- Delegate — hand raw text to Insight Digger.
- Analyze — sanity‑check the clusters.
- Delegate — pass insights to Draft Generator.
- Analyze — light copy‑edits, then ship.
A VoltAgent timeline view shows these hops as colored bars — it's like watching little factory robots on a conveyor belt. (Seriously, open console.voltagent.dev during a run. It's oddly satisfying.)
Key Code Excerpt
Why filter tools? Composio ships ~50 actions; passing them all bloats the tool‑spec messages. A simple filter saved ≈ 15% tokens per call.
The technical implementation uses:
- VoltAgent: A TypeScript framework that makes defining and coordinating AI agents straightforward
- Composio: A platform that simplifies connecting to apps like Reddit and HackerNews without writing individual API integration code
- OpenAI: Providing the models through the Vercel AI SDK, with strategic model choices for different tasks
The full source lives in the repo (link below), but here's the 10‑second boot script:
4 – Live Demo: "Agentic AI Frameworks"
I tasked my AI Content Strategist with this:
Prompt: "Using Reddit and Hacker News, find 3-5 pain points devs hit when building with agentic AI frameworks."
| Timeline Mark | What Happened | Real Excerpt |
|---|---|---|
| 00:03 s | Data Scout hits Composio ➜ Reddit API | 163 Reddit posts/comments, 42 HN threads fetched |
| 02:10 s | Insight Digger clusters text | "Integration headaches with legacy infra …" |
| 04:50 s | Draft Generator writes | "In recent months, 'agentic AI framework' has become the buzziest phrase on Hacker News …" |
| 06:12 s | Strategist signs off | ✔ FINAL_DRAFT.md |
Here's what the Insight Digger Agent returned:
Key Insights on Agentic AI Frameworks:
Pain Points:
- Integration challenges with existing systems
- Lack of comprehensive documentation
- Performance and reliability concerns
User Needs:
- Demand for better, more intuitive user interfaces
- Requests for more robust APIs for flexible integration
- Need for enhanced security features
The Draft Generator then took these insights and produced a surprisingly good first draft, starting with:
"In recent months, the concept of 'agentic AI frameworks' has sparked lively discussions across various tech communities, particularly on platforms like HackerNews. As AI continues to evolve, the need for frameworks that can support more autonomous and intelligent agents is becoming increasingly apparent..."
I still reread and tweak the prose (nobody wants robot‑sounding blog posts), but starting from a structured draft based on real community insights is chef's kiss compared to confronting a blinking cursor.
5 – The Big Win: Time Saved & Idea Sparked
This experiment wasn't about creating a perfect, autonomous content machine. It was about exploring the potential of AI agents to drastically reduce the upfront effort in content creation.
Here's what makes this approach valuable for both technical and business professionals:
- Rapid Market Pulse: Agents can quickly tap into community discussions to give you a snapshot of current sentiment and challenges.
- Overcoming "Blank Page Syndrome": Getting a solid, AI-generated first draft based on real insights means you're starting from a well-informed position, not a blank screen. This can be a huge morale and productivity booster.
- Focus on Higher-Value Work: If AI can handle the initial research and drafting, human marketers and product managers can spend more time on strategy, creative storytelling, adding unique perspectives, and refining the message.
- Discovering New Angles: The AI might surface connections or pain points you hadn't considered, sparking new content ideas or even product feature considerations.
6 – Lessons Learned (and Gotchas)
- Tools ≠ actions. VoltAgent treats each Composio action as a separate tool. Pass only what you need to save context window.
- Cheap models for I/O.
gpt‑3.5‑turbo‑0125is plenty for "download these comments"; save 4o tokens for thinking/writing. - Telemetry early, not late. The first time an agent stalls, a timeline trace beats printf‑debugging every time.
- Your mileage will vary. Topics with sparse Reddit chatter may need broader sources (Stack Overflow, dev.to, Discord logs…).
7 – Take It Further 🚀
- Support‑ticket Whisperer — swap Reddit/HN for Zendesk tickets to mine real user pain.
- Competitor Pulse Bot — scrape RSS + Tweets, auto‑summarize feature launches each morning.
- Internal Doc Drafter — feed Jira comments, spit out change‑log drafts.
- Cron & PR — run the pipeline nightly and open a pull request to your CMS repo.
The pattern stays the same: Scout → Digger → Drafter, with prompts tuned to your domain.
8 – Grab the Code & Try It
Repo: https://github.com/emretezisci/pmm-agent
9 – Your Turn → What Would You Automate?
Which part of your PM/PMM workflow feels like a hamster wheel? What time-consuming tasks in your product or marketing workflow do you think could benefit from an AI assistant?
Thanks for reading. Now go fire some helpful NPCs at your least‑favorite chore!