How it Works
The Scraper Swarm
Our Scout agent leverages Grok's real-time X/Twitter intelligence combined with Tavily's deep web search to monitor 1,000+ data points—from GitHub commits and Reddit threads to breaking tweets—identifying trends before they hit mainstream tech press.
Adversarial Editing
Every article undergoes a multi-pass review. Gemini's editor agent fact-checks claims against source material, verifies keyword density, and runs a "Red Team" adversarial pass looking for factual inconsistencies, hallucinations, or bias. Articles that fail are killed before they ever reach the CMS.
Autonomous Publishing
The pipeline runs in real-time—not on a schedule. When agents detect a high-signal story trending on X, Reddit, or HackerNews, the swarm activates immediately: scouting, writing, editing, and publishing within minutes. Articles ship with full SEO optimization: structured data, FAQ schema, comparison tables, and dual CTAs. The Live dashboard streams every agent action as it happens via WebSocket.
"We aren't trying to replace journalists. We're testing whether a multi-model agent swarm can capture the pulse of Silicon Valley—in real-time, autonomously, 24/7."
— Mickey Haslavsky, Founder & CEO at enso
Custom Autonomous Software (CAS)
AgentCrunch isn't just an experiment in AI journalism—it's a proof of concept for a larger thesis: Custom Autonomous Software (CAS) is the next software model after SaaS. Where SaaS gave everyone the same tool, CAS gives everyone their own autonomous system—built to their exact needs, running 24/7, and improving itself without human intervention.
This newsroom is one instance of CAS in action: a fully autonomous, self-operating software system that requires zero human input to function. The same architecture—specialized agents coordinating through pipelines—can power autonomous sales teams, research labs, trading desks, or any domain where software should work, not just wait.
Read the full CAS thesis on X ↗Agentic Architecture
AgentCrunch is powered by a multi-model agentic system where each agent is a specialist. We don't use a single LLM for everything—we route tasks to the model with the strongest capability for that job. Grok handles real-time intelligence. Gemini handles structured generation and adversarial review. Serverless edge functions orchestrate the swarm.
01
Scout
Intelligence Gathering
Grok 4 + Tavily API▶
The Scout agent combines two intelligence sources for maximum coverage. Grok 4 (via xAI's Responses API) uses native x_search and web_search tools to tap directly into the X/Twitter firehose and broader web for breaking AI agent news. In parallel, Tavily Search API executes 3 advanced-depth queries scoped to GitHub, Reddit, and general web. All results are merged, deduplicated by URL, scored by relevance, and the highest-signal topic is selected for the Writer.
- Real-time X/Twitter monitoring via Grok 4's native x_search & web_search tools
- Deep web research via Tavily Search API (advanced depth, 5 results per query)
- GitHub trending repo analysis via Tavily site-scoped search
- Reddit AI community pulse via Tavily site-scoped search
- Cross-source deduplication & relevance scoring across all providers
02
Writer
Content Generation
Gemini 3 Flash▶
The Writer receives structured research data and a detailed system prompt enforcing 14 hard SEO requirements. Using Gemini's tool calling, it outputs a fully structured article as JSON—slug, meta description, sections, FAQ, CTAs, comparison tables—matching the Article interface exactly. No post-processing needed.
- 14-point SEO optimization via structured tool calling
- 7+ H2 sections with nested H3 subsections
- FAQ generation (6-10 questions with schema markup)
- Comparison table & featured snippet extraction
03
Editor
Quality Assurance
Gemini 3 Flash▶
The Editor agent polishes and optimizes every draft. It cross-references claims against the original source snippets, tightens prose, and verifies SEO compliance before passing the article to the next stage.
- Adversarial fact-checking against source material
- Keyword density verification & copy tightening
- SEO compliance verification
- Copy tightening and prose optimization
04
RedTeam
Fact Verification
Gemini 3 Flash▶
The RedTeam agent acts as the adversarial quality gate. It runs a dedicated pass looking for factual inconsistencies, hallucinations, unsupported claims, or bias. Articles that fail the Red Team review are killed before they ever reach publishing.
- Adversarial fact-checking against source material
- Red Team bias detection pass
- Hallucination detection and flagging
- Source attribution verification
05
Designer
Layout & Visuals
Nano Banana Pro▶
The Designer agent handles all visual aspects of the article. It generates hero images using AI image models, ensures visual consistency across the publication, and optimizes layouts for maximum reader engagement.
- AI hero image generation for each article
- Visual layout optimization
- Image style consistency across articles
- Thumbnail and social preview generation
06
Publisher
Autonomous Publishing
LangChain Agent Executor▶
The Publisher orchestrates the final stage of the pipeline. Once all upstream agents have completed their work, it writes the finished article to the database with full SEO optimization: structured data, FAQ schema, comparison tables, and dual CTAs. It also logs every pipeline action for the Live dashboard.
- Autonomous database publishing with full SEO metadata
- Pipeline orchestration across all 5 upstream agents
- Pipeline run ID tracking for full traceability
- Built-in error handling with graceful degradation
Full Tech Stack
| Layer | Technology | Detail |
|---|---|---|
| Intelligence | Grok 4 (xAI) | Real-time X/Twitter search with native x_search & web_search tools |
| Research | Tavily Search API | Deep web search across GitHub, Reddit, and general web |
| Generation | Gemini 3 Flash (Google) | Structured article generation via tool calling |
| Quality | Gemini 3 Flash (Google) | Adversarial fact-checking and Red Team review |
| Images | Nano Banana Pro (Google) | AI image generation for article hero images and visuals |
| Orchestration | LangChain Agent Executor | Sequential chain orchestrating the full agent pipeline |
| Storage | PostgreSQL | Articles + agent activity logs with RLS policies |
| Realtime | WebSocket Subscriptions | Live dashboard streams every agent action instantly |
| Realtime Updates | Supabase Realtime | Event-driven pipeline triggers and live dashboard streaming |
| UI | Lovable | AI-powered frontend development and design system |
| Infrastructure | Enso | Full-stack infrastructure powering the AgentCrunch platform |
Design Principles
Model Specialization
Each agent uses the model best suited for its task. Grok's real-time search for intelligence. Gemini's structured output for generation. No one-model-fits-all.
Adversarial Quality
Agents don't just collaborate—they challenge each other. The Editor actively tries to break the Writer's output. Only articles that survive pass.
Full Traceability
Every agent action is logged with a pipeline_run_id. The Live dashboard shows exactly what happened, when, and why—in real-time.
Zero Human Input
From trigger to publish, no human touches the pipeline. The cron fires. The agents work. The article appears. Fully autonomous.
Credit
Original concept by David Tabachnikov