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Nederlands
$
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I will develop autonomous ai agents and workflows using n8n
Jonathan H
Over deze dienst
UPGRADE TO AGENTIC OPERATIONS: DEVELOP AI AGENTS IN n8n
Are linear, step-by-step automations failing when your data gets unpredictable?
Step away from rigid conditional logic and embrace agentic intelligence. By building autonomous AI agents within n8ns native advanced AI framework, you create intelligent systems that evaluate incoming scenarios, select their own tools, cross-reference custom vector databases, and self-correct errors in real time.
ADVANCED AGENTIC CAPABILITIES I DEPLOY:
- Native n8n Advanced AI Builds: Clean architectures leveraging Chat-LLM engines, advanced memory blocks, and vector embeddings.
- Contextual RAG Frameworks: Connecting agents natively to Pinecone, Qdrant, Supabase, or vector-mapped documents for accurate answers.
- Multi-Agent Systems: High-level Supervisor nodes that break down goals and delegate tasks to specialized sub-agents.
- Human-in-the-Loop: Pause-and-resume webhooks that alert your team via Slack for manual approval before high-impact actions.
PLEASE MESSAGE ME BEFORE PLACING AN ORDER to detail your target models, data vectors, and specific tool requirements!
Maak kennis met Jonathan H
Jonathan H
Full Stack Automation Engineer and AI Agent Architect
- Afkomstig uitVerenigde Staten
- Lid sindsjun 2026
- Gem. reactietijd6 uur
Talen
Engels
I am Jonathan Howard. Over the past 5 years, I have helped scaling businesses stop paying the expensive monthly task tax. I specialize in building custom AI automation workflows, setting up production ready n8n instances on secure VPS infrastructure via Docker, and migrating legacy setups from Zapier and Make to n8n to slash operational overhead by 90 percent. From engineering autonomous AI agents with vector memory to building automated lead generation engines, I build scalable systems optimized for enterprise performance. Message me today to unlock unlimited workflows.
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What is the difference between standard n8n workflows and your AI agents?
Standard workflows follow a rigid, step-by-step path (If X, then Y). AI agents use LLMs as a cognitive core. Given a goal, the agent dynamically chooses which tools to run, checks its own data quality, and adapts its path based on the context.
Which vector databases do you support for agent memory?
I natively integrate any vector framework supported by the n8n ecosystem, including Pinecone, Qdrant, Supabase, Milvus, and local Chroma instances, ensuring your agents scale with lightning-fast context lookups.
How do we prevent the AI agent from hallucinating or making mistakes?
We use structural guardrails: strict prompt engineering, forced JSON output schema parameters, and fallback validation nodes. If an agent output fails validation, the loop catches the error and passes it back to the LLM to self-correct.
Will I have to pay per execution or task for these AI agents?
You will pay directly for your LLM token usage (OpenAI/Anthropic API keys) and any cloud vector hosting. If you run n8n on a self-hosted server, you completely skip all middleware subscription and task fees.
Do you write custom JavaScript tools for the agents to use?
Yes. If your agent needs to fetch or push data to a non-native system, I build custom Code nodes or HTTP Requests. These are exposed to the agent as execution tools, allowing the LLM to run them whenever needed.
