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Multi-agent · Agentic RAG · MCP
AI Agent Development
We build AI agents that retrieve, reason and act inside your processes: connected to your systems and governed like any production system.
From prompts to production-grade agent systems
AI agents are no longer prompt wrappers with a tool call. They are long-running, goal-driven systems that plan, reason, retrieve and act, autonomously and at scale.
We design and build agent architectures that solve real business problems: multi-agent workflows that coordinate like a well-run team, agents that act on your systems through MCP and your integration layer, intelligence hubs that surface what matters, and content pipelines with human review.
What we build
Multi-agent systems
Specialist agents that collaborate under a coordinator: a researcher gathers data, a writer drafts, an analyst validates and a publisher releases. Built on LangGraph and CrewAI.
Agentic RAG pipelines
Retrieval that plans ahead. Our agentic RAG systems choose a retrieval strategy, route queries across knowledge bases, keep context across sessions and refine their answers, turning static document stores into living knowledge.
MCP-connected agents
The Model Context Protocol (MCP) is the open standard for connecting agents to tools and data. We build MCP-native agents that reach your CRM, ERP and analytics through standardised, auditable interfaces instead of brittle point-to-point code.
Enterprise intelligence hubs
Platforms that gather, process and present data for decision-makers. Agents work across internal metrics, market signals and competitive intelligence to deliver timely insights.
Content creation agents
Agents that research, draft, review and publish content across channels. They work to your brand voice, audience and editorial workflow, with human approval where it matters most.
In production
Built for agents
This website
kondevs.com runs a read-only MCP server with four tools and an A2A agent, protects its publishing API with OAuth and serves every page as markdown to AI agents. See the MCP server card.
Our approach
- Architecture first. Every agent system starts with a clear architecture: defined roles, tool boundaries, memory strategy and failure handling. We design for production from day one.
- Security by design. Agents that use tools open new attack surfaces. We scope permissions, audit tool access and monitor agent behaviour as rigorously as any production system.
- Enterprise integration. Our EAI roots mean we know how enterprise systems talk. We connect agent workflows to your platforms through APIs, middleware, event buses and MCP servers.
- Human in the loop. Full autonomy is not always the goal. We build in configurable checkpoints: review gates, approval flows and overrides.
Use cases
| Use case | What the agents do | Outcome |
|---|---|---|
| Automated content pipelines | Multi-agent workflows research, draft, review, optimise and publish content | Faster editorial cycles with a consistent brand voice |
| Customer intelligence | Analyse interactions across channels, surface patterns and trigger personalised engagement | Conversation data turned into revenue signals |
| Strategic decision support | Aggregate market data, internal KPIs and external signals into dashboards and briefs | Briefs leadership teams can act on |
| Knowledge management | Agentic RAG makes institutional knowledge searchable, contextual and actionable | Intelligent assistants instead of static wikis |
Technologies we use
- LangGraph
- CrewAI
- Model Context Protocol (MCP)
- Agentic RAG
- LLM orchestration
- Vector databases
- Python
- Node.js
Frequently asked questions
What does the KONDEVS AI Agent Development service deliver?
Production-grade AI agent systems that plan, reason, retrieve and act. Deliverables include multi-agent systems, agentic RAG pipelines, MCP-connected agents, content creation agents and enterprise intelligence hubs.
What is an agentic RAG pipeline?
Retrieval that plans ahead: the system chooses a retrieval strategy, routes queries across several knowledge bases, keeps context across sessions and refines its answers, turning static document stores into living knowledge.
How does KONDEVS approach agent security and human oversight?
Agents are designed for production from day one, with scoped permissions, audited tool access and behaviour monitoring. Systems include configurable human-in-the-loop checkpoints: review gates, approval flows and overrides.
What technologies are used for AI agent development?
LangGraph, CrewAI, the Model Context Protocol (MCP), agentic RAG, LLM orchestration, vector databases, Python and Node.js. Agent workflows connect to enterprise platforms through APIs, middleware and MCP servers.
Can agents connect to SAP, CRM or ERP systems?
Yes. Our integration roots let agents reach your systems through APIs, middleware, event buses and MCP servers, including SAP ERP and S/4HANA through webMethods and SEEBURGER.
Insights on this service
EAI & Integration
What Is Enterprise Application Integration and Why It Matters in 2026
Enterprise Application Integration is the connective tissue of modern enterprise IT. Here's what EAI actually is, the four patterns that make it work, and why (in the age of AI agents) it matters more than ever.