Custom agent architecture
We design agents that decide according to your business rules — and hand over to a human whenever they are not confident.
[ Service ]
AI Agent Development
We build custom AI agents that understand context, make decisions, and act autonomously within defined guardrails.
[ Scope ]
What's Included
Knowledge base and RAG setup
We ground the agent in your documents, CRM records and past conversations, so answers are based on your data.
Tool use and controlled actions
Agents open tickets, update records and send emails — always within explicitly defined permission boundaries.
Human oversight and guardrails
Approval steps on critical actions, validated outputs and auditable decision logs come as standard.
Production-grade deployment
With LangChain and modern AI tooling we ship agent infrastructure that is logged, tested and scales under load.
Puraeon’s AI agent development service designs and ships custom AI agents that understand context, make decisions and act autonomously within clearly defined permissions. Our agents don’t just talk — they open tickets, update records and move processes forward end to end.
Our approach
An agent’s value comes from the actions it performs safely, not from the answers it gives. So we start by defining not what the agent can do, but what it is allowed to do. Every agent gets a clear scope of work, the data sources it may access and the tools it may use — anything outside that boundary is blocked at the architecture level.
We start with a fast prototype, test it against your real data, then move to production. Every decision the agent makes is logged, and handing over to a human — with a summary of the conversation — is a hard rule whenever it is uncertain.
What we deliver
- Agent design with defined scope, permission boundaries and escalation rules
- A RAG-based knowledge layer fed by your documents and system records
- Tool integrations with your CRM, helpdesk and internal apps
- Approval flows, logging and a performance dashboard
- Post-launch monitoring and continuous improvement
Who it’s for
Ideal for customer teams drowning in support requests, sales operations where quotes and records are updated by hand, and HR or ops teams answering the same internal questions over and over. Wherever a chatbot isn’t enough and real action is required — that’s agent territory.
Tools we use
We use LangChain and modern LLM APIs at the agent layer; vector search on Supabase and PostgreSQL for the knowledge base; and n8n plus custom Python services for orchestration. Infrastructure runs on Vercel or AWS, with observability built in from day one.
Frequently Asked Questions
What is the difference between an AI agent and a chatbot?
A chatbot answers questions; an agent understands context, makes decisions and takes action — opening tickets, updating records, moving processes forward. We add human approval on critical steps.
How do you prevent agents from making mistakes?
We work with validated outputs, access controls and human approval steps. Every decision is logged, and when the agent is unsure it hands the task to a human.
Ready to Start Your AI Project?
Let’s explore how AI can boost your workflow and business performance.