5 things you need to know about agent-to-agent AI collaboration (www.techradar.com)

Enterprises are shifting from single-assistant AI to ecosystems of autonomous, goal-driven agents that can reason, delegate and collaborate—essentially encoding team-like orchestration into software. Rather than simple task automation, agentic systems plan multi-step workflows, pick appropriate tools, adapt when blocked and divide labor (e.g., data-gathering, risk assessment, execution). This change promises faster, scalable operations across software development, logistics, healthcare and finance, but it also forces CIOs to reconceive AI as connective infrastructure, not an add-on. Delivering on that promise requires new technical foundations and governance. Firms need API-first, event-driven architectures, semantic data layers, service meshes and identity/permission frameworks so agents can discover, trust and invoke each other (new protocols cited include MCP, A2A and AP2). Multi-LLM interoperability and model-arbitration policies are essential—agents will orchestrate heterogeneous models and maintain context windows. Coordination layers or meta-agents (early tools: LangGraph, CrewAI) must assign roles and avoid duplicated or conflicting work; brittle orchestration built as DSLs in Python/TypeScript risks short-lived solutions. Finally, persistent experience—semantic memory layers, context persistence and feedback loops—is critical so agent interactions become reusable knowledge, not ephemeral logs. CIOs who invest in robust orchestration, interoperability and memory will be best positioned to harness agent-to-agent AI safely and at scale.