AI Agent Frameworks
The orchestration layer behind agentic AI — frameworks and protocols that let models use tools, hold memory, collaborate and act reliably, with the guardrails and observability that make autonomy safe.
Where this fits in your AI stack
Agents are only as good as the framework orchestrating them. Tooling like LangGraph, CrewAI, AutoGen and the Model Context Protocol (MCP) handle the hard parts — tool use, memory, multi-agent collaboration, retries and state — while guardrails and tracing keep autonomy safe. We choose and implement the right stack so your agents are reliable, observable and production-ready, not a fragile demo.
The engine room of agentic AI
Frequently asked questions
Which agent framework should we use?
It depends on your needs — LangGraph for controllable graph-based workflows, CrewAI/AutoGen for multi-agent collaboration, and MCP for standardised tool access. We pick based on reliability, scale and your stack, not hype.
What is MCP (Model Context Protocol)?
MCP is an open standard for connecting models and agents to tools and data sources in a consistent, secure way — reducing bespoke integration work and making capabilities reusable across agents.
How do you make agents reliable enough for production?
With guardrails, evaluation suites, full tracing of every action, retries and human-in-the-loop on sensitive steps — the same engineering discipline we bring to any production system.