$ agentled create "Outbound to fintech CTOs in Europe"

Loading workspace context from Knowledge Graph...
✦ ICP loaded  ✦ 3 prior campaigns  ✦ 847 contacts in KG

Creating campaign with 3 workflows...

━━ Workflow 1: Prospect Research  linkedin · hunter · clearbit
  ✓ LinkedIn: CTO + fintech + EU → 189 profiles
  ✓ Enriched via Hunter + Clearbit → 156 matched
  ✓ ICP scoring → 43 high-intent leads

━━ Workflow 2: Signal Detection  web-scraper · crunchbase
  ✓ Job postings → 12 hiring devops
  ✓ Crunchbase → 8 recently funded
  ✓ Cross-match: hiring + funded → 5 hot leads

━━ Workflow 3: Outreach  email · linkedin · kg
  ✓ Personalized emails from context
  ✓ LinkedIn requests with custom notes
  ✓ 43 leads saved to Knowledge Graph

Campaign saved. Scheduled: every 48h
Credits used: 720
→ https://www.agentled.app/your-team/fintech-cto-outbound

claude mcp add agentled \
  -e AGENTLED_API_KEY=wsk_... \
  -- npx -y @agentled/mcp-server

cd agentled-mcp-server
npm run build

claude mcp add --transport stdio agentled_local \
  --env AGENTLED_API_KEY=wsk_... \
  --env AGENTLED_URL=http://localhost:8080 \
  -- node /absolute/path/to/agentsled-front/agentled-mcp-server/dist/index.js

Run 1:  Investor scoring → 62% accuracy (cold start)
Run 5:  → 78% (learning from IC feedback)
Run 12: → 89% (compound learning from outcomes, zero manual tuning)

"Find fintech CTOs in Europe, enrich via LinkedIn + Hunter, score by ICP fit,
draft personalized outreach, save everything to the Knowledge Graph"

"Scrape trending topics in our niche, generate 5 LinkedIn posts with AI,
create thumbnail images, schedule publishing for the week"

"Research this company from its URL — team, funding, market position, competitors.
Generate an investment memo. Store in KG for future reference."

"Match this startup against our 2,000+ investor database. Score by sector focus,
stage preference, check size, and portfolio synergy. Compare with last round's outcomes."

"Set my workspace branding: displayName 'Acme AI', primaryColor '#6366f1', tagline 'Powered by Acme'"

"Store a memory: key 'icp_criteria', value { industry: 'fintech', minEmployees: 50, region: 'EU' },
category 'preference', scope 'workspace'"

"Recall memory 'icp_criteria' at workspace scope — use it to score this batch of leads"

"Search memories for 'conversion rate' in the 'outcome' category"

"Store memory: key 'total_leads_processed', value 43, merge 'increment', scope 'workspace'"

"Create a proactive agent named 'New Lead Watcher' that checks the 'incoming-leads' knowledge list
every 5 minutes. When new rows appear, start the 'lead-enrichment' workflow with the new rows as input.
Limit to 10 actions per day."

{
  "monitorInterval": "5m",
  "evaluation": { "mode": "rules" },
  "monitors": [{
    "type": "kg_list",
    "listKey": "incoming-leads",
    "condition": "new_rows"
  }],
  "actions": [{
    "type": "start_workflow",
    "workflowId": "wf_abc123",
    "inputMapping": { "leads": "{{monitor.newRows}}" }
  }],
  "maxActionsPerDay": 10,
  "cooldownMs": 300000
}

"Create a proactive agent that checks execution history every hour.
Use AI evaluation to decide if the failure rate is abnormal, then notify me via email."

{
  "monitorInterval": "1h",
  "evaluation": { "mode": "ai", "modelTier": "mini", "maxCreditsPerDay": 50 },
  "monitors": [{
    "type": "execution_history",
    "condition": "consecutive_failures",
    "threshold": 3
  }],
  "actions": [{
    "type": "notify",
    "channel": "email",
    "message": "{{monitor.summary}}"
  }],
  "maxActionsPerDay": 5
}

"Pause proactive agent pa_xyz789"
"Resume proactive agent pa_xyz789"

git clone https://github.com/Agentled/mcp-server.git
cd mcp-server
npm install
npm run build