Natural-Language Agent Builder
Describe an agent in plain English and Neblex drafts the tools, decision logic, and approval checkpoints, ready for you to review and refine.
The problem
Building an agent usually means wiring together tools, prompts, and guardrails by hand across several systems. Teams that know exactly what they want still lose significant time translating that intent into configuration. The result is a backlog of automation ideas that never get built.
How it runs on Neblex
On Neblex, you describe the agent you want in plain English: what it should watch, which tools it may call, and where a human must sign off. The platform drafts the agent for you, including the tool chain, the decision logic, and the approval checkpoints. You review the draft and refine it by describing changes, not by editing configuration.
The drafted agent can use any of the 236 connectors as tools, query databases with SQL, use Data Tables, invoke flows, and automate a real browser for systems without APIs. You bring your own LLM: any OpenAI-compatible endpoint, including self-hosted models on your own infrastructure, or Anthropic. The platform is not locked to one provider.
Every action the finished agent takes is logged to a tamper-evident hash-chained audit trail, and any action can be gated behind human approval before it executes. Environments with promotion approvals let you test the agent before it touches production systems.
Step by step
Describe the agent
Write what the agent should do in plain English, including where humans must approve before an action runs.
Review the draft
Neblex drafts the tool chain, decision logic, and approval checkpoints for your review.
Refine by describing changes
Ask for adjustments in plain language and the draft updates. No configuration editing is required.
Pick your model
Point the agent at any OpenAI-compatible endpoint, a self-hosted model on your own infrastructure, or Anthropic.
Test before production
Run the agent in a lower environment first. Promotion to production goes through approval.
Promote and monitor
Promote through the approval gate and follow every action in the hash-chained audit trail.
Platform capabilities used
- Describe-to-build agent drafting
- Bring-your-own-LLM model choice
- Human-in-the-loop approvals
- 236 connectors as agent tools
- Environments with promotion approvals
- Hash-chained audit trail
Common questions
Do we need to write code to build an agent?
No. You describe the agent in plain English and Neblex drafts the tools, decision logic, and approval checkpoints. You refine the draft by describing changes and review everything before the agent runs.
How do we keep a drafted agent from acting without oversight?
Any agent action can require human approval before it executes. Reviewers see the agent reasoning and the proposed action in a task inbox with SLA timers and escalation, and every action is logged to a tamper-evident hash-chained audit trail.
Which models can the agent use?
Any OpenAI-compatible endpoint, including self-hosted models running on your own infrastructure, or Anthropic. The platform is not locked to one provider, so you can change models without rebuilding the agent.
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Want this running on your stack?
Neblex Integration Fabric is in beta: full platform, free while in beta. Bring this workflow and we will map it to your systems.