What is Agentic AI

An agentic AI is an agent that can reason, plan, and act autonomously while being governed and controlled.

Unlike AI assistants, which primarily respond to user queries and follow predefined workflows, agentic AI can independently make decisions, set goals, and execute tasks while remaining aligned with human intent and constraints.

LLMs in agentic AI

At the core of AI agents are large language models (LLMs)—powerful neural networks trained on vast amounts of text to generate human-like responses. They excel at reasoning, summarization, and decision-making, making them a critical component of agentic AI.

LLMs have two fundamental limitations that require careful engineering to be effective in enterprise-grade agents:

LLMs are stateless. They don’t retain memory between interactions, meaning agents must incorporate a persistent memory system to track both short- and long-term context.
LLMs are slow and unreliable. Unlike traditional software, which operates in milliseconds, LLMs can take seconds per response and are prone to variability in output quality. Agents need orchestration layers to handle execution reliability, caching, and optimization.

SaaS apps vs agentic AI services

SaaS Applications	Agentic AI
Transaction-centered: short, stateless interactions	Conversation-centered: Long-lived, stateful interactions
Immediate response: system processes request and returns a result	Context-aware responses: system remembers past interactions to stay relevant
Minimal compute and memory per transaction	High compute and memory usage per conversation
Predictable scaling: More users = more transactions = predictable load	Unpredictable scaling: More users lead to exponentially higher workload due to longer interactions and agent-drive actions

Traditional enterprise applications follow an N-tier architecture, designed for structured transactions and deterministic workflows. But agents operate in an entirely different paradigm—conversational, adaptive, and stateful. To support this, enterprises need an agentic tier (A-tier) architecture, which runs alongside the existing N-tier stack.

In the A-tier architecture, swarms of agents run autonomously, managing their own state and coordinating with each other. To function effectively, agents depend on A-tier infrastructure, which provides five critical capabilities: agent lifecycle management, agent orchestration, memory, streaming, and integrations.

This N+A-tier approach ensures that enterprises can harness the power of agents without disrupting their existing technology stack, enabling intelligent automation at scale.

Agentic tier capabilities

Durable workflows that manage long-running, multi-step processes, ensuring agent actions and LLM calls execute reliably, even in the face of hardware failures, timeouts, hallucinations, or restarts.

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Persistent short- and long-term memory to maintain context across conversational interactions.

A streaming architecture enables agents to process and respond quickly to high data volumes such as video, audio, IoT data, metrics, and event streams, gracefully handling LLM latency and ensuring responsiveness. Streaming is also critical for supporting ambient agents—AI agents that continuously monitor event streams and only respond when necessary, enabling proactive, context-aware actions rather than reactive, chat-style interactions.

Native connectivity with enterprise APIs, databases, and external tools to extend agent capabilities, leveraging established standards like OpenAPI and emerging ones like the Model Context Protocol, which define how agents provide context and tools to LLMs.

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