We leverage cutting-edge frameworks including CrewAI, AutoGen, and LangGraph to deliver production-grade autonomous systems.
We don\'t believe in one-size-fits-all. Our architects select the best framework for your specific use case, ensuring scalability, observability, and cost-efficiency.
CrewAI & AutoGen: For sophisticated multi-agent task delegation and hierarchical management.
LangGraph: For cyclical multi-step workflows with fine-grained state management and persistence.
OpenAI, Claude & Llama 3: Powered by the world\'s most capable reasoning models.
Agent breaks goal into sub-tasks.
Agent performs actions via tools.
Agent reviews output for errors.
Human receives a validated result.
Our engineering teams also specialize in building scalable, autonomous systems leveraging top-tier AI frameworks. Depending on your core architecture, we actively integrate and utilize AI Development, Generative AI, LangChain Development to deliver robust, future-proof applications. Read our related guides: How to Build AI Agents with LangChain, AI Agents vs Traditional Automation, Multi-Agent AI Systems Architecture, and AI Coding Agents & Developer Productivity.
Agentic AI development is the engineering of autonomous systems where an AI model plans, executes, and adapts multi-step workflows without constant human input. Unlike a chatbot that answers a single prompt, an agentic system receives a goal — such as 'process all incoming support tickets and escalate anything critical' — and completes it end-to-end using tools like APIs, databases, and web search.
A focused agentic workflow (single agent, 2–4 tools, defined scope) typically takes 4–8 weeks from kickoff to production. A multi-agent system with persistent memory, human-in-the-loop checkpoints, and enterprise integrations typically takes 10–16 weeks. Inventiple's senior-led teams consistently deliver production-ready agentic systems in 8–12 weeks.
We primarily use LangGraph for stateful multi-agent orchestration, CrewAI for role-based agent collaboration, and AutoGen for conversational agent pipelines. The right framework depends on your use case — LangGraph excels at complex workflows with branching logic, CrewAI works well for specialist agent teams, and AutoGen suits dialogue-heavy automation.
Traditional automation (RPA, rules-based scripts) follows fixed, deterministic paths — it breaks when inputs are unpredictable. Agentic AI handles unstructured inputs, makes contextual decisions, and self-corrects on failure. Traditional automation is cheaper for simple, stable workflows. Agentic AI is the right choice when the task requires judgment, varies with each execution, or involves unstructured data like emails, documents, or voice.
A single-agent MVP with defined tool integrations starts at approximately $15,000–$40,000. A production-grade multi-agent system with memory management, guardrails, monitoring, and enterprise integrations typically costs $50,000–$150,000+. Ongoing API and infrastructure costs range from $500–$5,000/month depending on usage volume.