AgentLed is a workflow automation platform where AI agents own, manage, and improve workflows. It provides 100 integrations through a unified credit system, pe

Joined August 2024
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Agents fail when they must be reliable. Automations fail when tasks need judgement. They’re not alternatives — they solve different problems.
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Agents = planning/search loops (LangChain-style). Good for exploration, recovery and orchestration. Expensive, hard to control, and they leak state if you don’t design memory explicitly.
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Design rule: run deterministic side effects as automations; use agents as thin decision layers for exceptions, orchestration, or search. Keep heavy state in deterministic stores and bound agent memory. Full breakdown our ops trade-offs: agentled.ai/en/blog/post/ai-…
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Automation is entering its third era: AI agents that operate, humans that supervise.
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Memory matters. Vector search is fast context recall — not authoritative memory. Use knowledge graphs for persistent facts and auditability, vectors for short-term context. Hybrids reduce hallucinations and debugging time.
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1/ Run your n8n workflow 100 times. What did it learn? Nothing. Every run starts from zero. Same inputs, same logic, same mistakes. You’re not automating a process. You’re repeating one. 👇
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10/ Not every workflow needs memory. Stateless is fine for: - Slack alerts - CSV transforms - welcome emails - deterministic routing You need memory for: - lead scoring - outreach optimization - content feedback loops - anything that should improve over time.
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Count your API subscriptions. LinkedIn. Hunter. OpenAI. Apify. Maybe Clearbit. Maybe a scraping tool you set up in November and forgot about. Separate billing pages. Separate rate limits. Separate auth flows. You're not building workflows. You're managing vendors. 👇
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If you live in the terminal: npx @agentled/cli setup agentled create "Outbound to fintech CTOs in Europe" Natural-language brief → planned, built, validated workflow. 100 services under one key. No SDK installs. No 8 env vars.
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