Agentic AI
Definition
Agentic AI refers to AI systems that autonomously execute multi-step tasks toward a defined goal, using reasoning, tool use, memory, and self-correction. Agentic AI moves beyond chatbots that respond to systems that act.
Why it matters
The business case for Agentic AI.
Gartner projects 40% of enterprise applications will feature task-specific AI agents by 2026, up from less than 5% in 2025. Agentic AI is the architectural shift defining the next phase of enterprise AI.
How SynthesisArc applies it
From concept to production.
PRISM supports enterprise-grade agentic AI with deterministic guardrails, the only safe way to deploy agents in production operations.
Go deeper
Field Notes on Agentic AI.
Related terms in AI Architecture
Cognitive Architecture
A Cognitive Architecture is the structural design of an AI reasoning system, including how it perceives input, accesses memory, plans actions, and learns from feedback. Cognitive architectures are what differentiate sophisticated AI from simple model wrappers.
PRISM
PRISM is SynthesisArc's seven-layer cognitive architecture for enterprise AI. The layers, perception, context, memory, reasoning, planning, action, and learning, combine deterministic and generative AI to deliver consistent, auditable outcomes.
LLM (Large Language Model)
A Large Language Model (LLM) is a foundation model trained on massive text datasets to predict and generate language. GPT, Claude, Gemini, and Llama are all LLMs.
Multi-Agent System
A Multi-Agent System is a coordinated set of AI agents working together on a shared goal, sharing context, handing off tasks, and avoiding conflicts. Multi-agent systems are required for any workflow that crosses departmental or functional boundaries.
RAG (Retrieval-Augmented Generation)
Retrieval-Augmented Generation (RAG) is an architecture pattern where an AI system retrieves relevant information from a knowledge base and uses it to inform generated responses, grounding the AI in specific, up-to-date data.