SourceFrom retrieval to agents: 5 takeaways on production architecture for AI agents
Low trust in factual accuracy currently prevents over 50% of companies from moving their agentic AI trials into full production.
- IDC research quantified that only 12% of companies are always confident in the factual accuracy of answers from primary discovery tools.
- Over 50% of companies struggle to move agentic AI from trials to full production due to trust gaps in agent outputs.
- Model Context Protocol (MCP) and Agent2Agent (A2A) were highlighted as emerging standard interfaces for connecting agents to data and facilitating collaboration.
- Context engineering was presented as the essential discipline to prevent context rot and improve accuracy in agentic systems.