The Ultimate Guide to Agentic AI

What is agentic AI?

Agentic AI refers to artificial-intelligence systems that can autonomously sense, reason, plan, and act to achieve goals — not just respond to inputs. Unlike traditional models that wait for a prompt, agentic systems are active participants that make decisions, take initiative, and coordinate with other agents or systems to drive outcomes. They operate continuously and contextually across dynamic environments, consuming live data, adapting plans in flight, and generating new events as they act.

Agentic AI is the next stage in the evolution of artificial intelligence — from systems that analyze, to those that generate, to those that can act:

• Traditional AI aka Machine Learning — Models trained to solve narrow, well-defined problems; capable of classifying data or predicting outcomes but confined to static datasets.
• Generative AI — Large Language Models able to understand and create text, images, and code; reactive and creative, but not goal-directed.
• Agentic AI — Autonomous systems that combine reasoning, memory, and real-time awareness to plan and execute actions using tools, APIs, and event streams — even when there’s no predefined path.

That leap — from reacting to acting — is why organizations are so excited about agentic AI’s potential to transform how work gets done.

Why are enterprises embracing agentic AI?

Enterprises are evolving beyond isolated AI features toward autonomous digital workers that handle complexity end to end. Agentic AI merges analytics with execution, allowing intelligence to live inside business processes instead of around them.

What are main reasons for adopting agentic AI:

• Automation of multi-step workflows — Agents can plan, execute, and adjust tasks in real time without manual oversight.
• Continuous adaptation — They consume live data to respond to events as they unfold.
• Cross-system collaboration — Agents communicate through shared events and APIs to coordinate across silos.
• Scalable decision-making — Hundreds of agents can operate in parallel, each optimized for a specialized function.

What are examples of agentic AI in action?

Agentic AI is already transforming industries:

• IT Operations & Reliability — Monitoring agents detect anomalies and trigger fixes automatically, reducing downtime.
• Customer Experience — Support agents access CRM, inventory, and knowledge bases in real time to resolve cases proactively.
• Supply Chain & Logistics — Planning agents re-route shipments and reorder stock based on live conditions.
• Finance & Risk — Compliance agents watch transactions and markets, executing rules instantly when thresholds are hit.
• Software Development — Code, test, and deployment agents coordinate to ship and roll back software autonomously.

Each example follows the same pattern — perceive events, reason about them, coordinate via A2A messaging, and act through MCP-connected tools. More on those below.

How does agentic AI work?

Agentic AI operates through a continuous sense–think–act loop — often described as perceive → reason → plan → act.

• Perceive — Agents collect live signals from sensors, APIs, and event streams.
• Reason — LLMs and domain logic interpret inputs and infer next steps.
• Plan — Agents decompose goals, select actions, and sequence tools.
• Act — They execute API calls, publish events, and update systems — generating new data that restarts the loop.

When many agents share context through an event mesh, they form a living ecosystem of intelligence that learns and reacts continuously.

What does it take to implement agentic AI?

Agentic AI depends on several core technologies that provide perception, communication, memory, and control. Together they form the foundation for real-time, distributed intelligence.

What is an AI app framework?

An AI-app framework is a structured foundation — a toolkit of libraries, APIs, abstractions and runtime scaffolding — built to enable developers to build, deploy, and orchestrate intelligent applications powered by machine learning (ML) or large language models (LLMs). Rather than coding every piece from scratch (data handling, model loading, memory/state, integrations, orchestration), an AI-framework gives you reusable building blocks so you can focus on higher-level application logic and value.

These frameworks operate at different layers: some provide low-level ML infrastructure (training, model evaluation, inference), others provide higher-level orchestration around LLMs and AI flows (context, data integrations, retrieval, agents, workflows). The key benefit is speed, modularity, scalability, and maintainability when building AI applications.

The world of AI frameworks spans a continuum of abstraction — depending on whether you’re working directly with ML models or building full applications around LLMs and AI logic:

• Model-Level Frameworks — build and train ML models
  Tools like PyTorch or TensorFlow provide deep control over neural networks, data pipelines, training, and inference. Best when you need to design or fine-tune models and work close to the math.
• LLM-App Frameworks — build features on top of large language models
  Frameworks like LangChain help integrate LLMs with external data or tools, manage memory/state, and orchestrate multi-step logic such as retrieval-augmented generation, chat flows, and agent behaviors.
• Data-Centric LLM Tooling — connect LLMs to enterprise data
  Tools like LlamaIndex focus on ingesting, indexing, and retrieving data from documents, databases, and APIs so LLMs can provide context-aware answers grounded in your own information.
• Full-Stack AI-App Platforms — build and run production AI systems
  Platforms like Solace Agent Mesh provide the whole environment: orchestration, real-time data movement, memory, integrations, deployment, scale, and governance — enabling complex AI agents and end-to-end intelligent applications.

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What is an agent mesh?

Real-time context is the lifeblood of agentic AI. Without it, agents act blindly — unable to perceive change or coordinate decisions as the world evolves. With it, they can respond to live events, adapt plans instantly, and collaborate with other agents toward shared goals.

An agent mesh is the real-time data fabric that keeps autonomous agents aware, connected, and coordinated. It ensures every agent — regardless of where it runs — can see the latest events, share state, and act on live context.

Built on event-driven principles, an agent mesh moves information between agents as streams of events, not one-off API calls. This allows perception, reasoning, and action to happen continuously instead of sequentially. The core capabilities of an agent mesh include:

• Event distribution — Streams real-time data between agents, tools, and systems as events occur.
• Context propagation — Carries metadata and correlation IDs so every agent understands who did what, and when.
• Dynamic discovery — Lets new agents register, subscribe, and participate instantly without manual wiring.
• Reach and reliability— Delivers guaranteed, ordered messaging across clouds, regions, and runtime environments.

An agent mesh gives AI systems the same kind of connective tissue that a service mesh provides for microservices — but tuned for autonomy and real-time intelligence. It transforms isolated agents into a cohesive, event-driven ecosystem that learns, reacts, and collaborates as one distributed brain.

What Is A2A?

A2A (Agent-to-Agent) is a protocol that was introduced by Google DeepMind in 2024 to standardize how autonomous AI agents communicate, collaborate, and share reasoning steps. It defines a structured message format and interaction model that lets agents exchange goals, tasks, and results securely — much like APIs did for application integration.

A2A provides:

• Interoperability — A shared schema and ontology for multi-agent coordination, regardless of vendor or platform.
• Transparency — Message-level tracing for observability and audit.
• Scalability — Lightweight communication that supports swarms of cooperating agents.
• Security — Authentication and authorization across agent networks.

Originally developed for multi-agent reasoning experiments, A2A has since been implemented in open frameworks like LangChain, CrewAI, and Solace Platform, where it extends into enterprise-grade messaging and observability. Together, A2A and MCP form the nervous system of agentic systems — A2A for inter-agent dialogue, MCP for execution and external action.

What is MCP?

Agents need a standard way to access tools and data. MCP — short for Model Context Protocol — is that standard. Introduced by Anthropic in late 2024 and endorsed by OpenAI, Google, and Microsoft, MCP enables AI systems to connect to external APIs, databases, and services through a single, declarative interface.

MCP enables:

• Unified integration — One protocol for accessing any tool or dataset.
• Built-in governance — Control which agents can invoke what, with centralized oversight.
• Real-time interaction — Streamable HTTP and SSE transports for responsiveness.

In the Solace Agent Mesh, MCP servers appear as tools in an agent’s configuration file. The Solace Platform handles discovery, registration, and protocol translation automatically — letting developers describe capabilities while the agent handles invocation.

MCP is open-sourced on GitHub under the Model Context Protocol organization, with reference implementations like @modelcontextprotocol/server. It reduces integration friction and makes enterprise data instantly usable inside agentic workflows.

What Is LiteLLM?

Organizations often use multiple large language models. LiteLLM — created by BerriAI in 2023 — is the open-source abstraction layer that unifies how developers connect to those models. It provides a single API compatible with over 100 providers, including OpenAI, Anthropic, Mistral, Gemini, Groq, and Ollama.

As of late 2025, LiteLLM sits at version 1.34 and is integrated into frameworks like LangChain, CrewAI, and LlamaDeploy. It’s become the de facto open standard for multi-model routing because it mirrors the OpenAI API schema — meaning developers can swap models without refactoring.

Other tools in the multi-model ecosystem include:

• OpenRouter — a hosted multi-model API with unified billing.
• Hugging Face TGI — inference server for open-source model hosting.
• Ollama / vLLM / Groq API — optimized runtimes for local or hardware-accelerated deployment.
• OpenAI API Schema — the base structure many APIs emulate.

Together, LiteLLM and MCP bridge reasoning and action: one standardizes how agents think across many models; the other standardizes how they act across many systems.

What is a vector database?

A vector database gives agents memory — the ability to recall by meaning, not just exact words. Systems like Pinecone, Weaviate, and Chroma store semantic embeddings so agents can ground decisions in past experience.

Typical memory layers include:

• Short-term context — Immediate recall from current tasks or chats.
• Long-term memory — Persistent embeddings in vector stores.
• Shared memory — Team-level recall for multi-agent systems.

By combining vector memory with real-time events, agents can reason over both what just happened and what has happened before — enabling contextual awareness across time.

What is agent orchestration and governance?

As agent networks grow, coordination and control become essential. Orchestration frameworks define how agents collaborate and how their actions are monitored.

Key functions include:

• Workflow design — Sequencing tasks and dependencies across agents.
• Observability — Emitting events for audit and debugging.
• Governance — Policies that limit access and ensure compliance.

Frameworks such as LangGraph, CrewAI, and Semantic Kernel manage workflows, while the Solace Platform delivers the real-time event flow that powers those interactions. Together, they create a feedback loop of visibility and accountability that keeps autonomy safe.

What’s next in agentic AI?

Agentic AI marks the shift from systems that analyze to systems that act. As real-time data flows through event-driven infrastructure and standards like MCP, A2A, and LiteLLM mature, organizations will build living networks of agents that reason, plan, and respond as fast as the world changes.