Marktechpost Releases 2025 Agentic AI and AI Agents Report: A Technical Landscape of AI Agents and Agentic AI

Marktechpost AI Media has unveiled its most comprehensive publication—The Agentic AI and AI Agents Report for 2025—delivering a technically rigorous exploration into the architectures, frameworks, and deployment strategies shaping the future of AI agents. The report spans the full agentic AI stack, mapping out a growing ecosystem built on reasoning-capable models, memory frameworks, and orchestration engines purpose-built for real-world tasks.

Redefining AI with Autonomy

Unlike conventional assistants, agentic AI systems are defined by their ability to operate independently, make decisions, and learn over time. These agents are not just language model wrappers—they integrate planning, tool use, multimodal understanding, and persistent memory. The shift from prompt-based interaction to autonomous goal execution marks a foundational evolution in AI utility.

Agents act with clear intent: executing tasks, synthesizing context across modalities, collaborating with humans or other agents, and iteratively refining their strategies. This proactive behavior distinguishes them from bots or assistants that rely on preprogrammed logic or reactive instruction following.

Agent Architecture: A Modular Stack

The report dissects the anatomy of modern AI agents into distinct, modular components:

• Model (Core Reasoner): LLMs and multimodal transformers that generate, interpret, and reason over high-level objectives.
• Tool Interfaces: APIs, browsers, and databases used by agents to interact with digital environments.
• Memory Systems: Mechanisms for episodic and semantic memory enabling long-term coherence and personalized behavior.
• Persona & Intent Layer: Role-based behavioral modeling that guides tone, task scope, and interaction design.
• Orchestration Layer: Manages state, workflow execution, retries, and inter-agent communication across distributed environments.

This architecture supports both single-agent pipelines and collaborative multi-agent systems designed for coordinated task execution in complex enterprise workflows.

Agent Development Frameworks

Marktechpost’s report catalogs over 25 production-grade platforms and frameworks. Notable among them:

• CrewAI: A high-performance multi-agent framework offering low-level control, ideal for enterprise-grade orchestration.
• LangGraph: A graph-based framework enabling stateful, streaming agent workflows with built-in observability and moderation hooks.
• Google Vertex AI Agent Builder: Offers a managed runtime with the Agent2Agent (A2A) protocol for cross-framework agent interoperability.
• Salesforce Agentforce: Built atop Data Cloud, it supports action orchestration across CRM systems with trust and compliance by design.

These platforms demonstrate diverse approaches—from no-code prototyping to code-first orchestration—while aligning around common principles: memory retention, tool interoperability, and composable logic.

Infrastructure, Evaluation, and Observability

The report addresses the broader operational stack underpinning agentic systems:

• Model Serving & Hosting: Platforms like Fireworks AI, Baseten, and OpenRouter provide optimized inference APIs and infrastructure for large and small models.
• Memory Engines: Solutions like ZEP, Whyhow.ai, and Contextual.ai introduce structured memory mechanisms optimized for dynamic information retrieval and adaptive planning.
• Evaluation & Safety: Tools such as Patronus AI, Haize Labs, and Inspeq AI offer evaluation frameworks, traceability, hallucination detection, and failure prediction—key for trust and compliance.
• Observability Layers: Frameworks like AgentOps deliver real-time tracing, cost analysis, and debugging capabilities across LLM and multi-agent deployments.

Of particular note is Unsloth AI, an open-source toolkit for low-cost fine-tuning and quantization of open models like LLaMA and Qwen. It enables developers to train domain-specialized agents using synthetic data—entirely offline and on consumer-grade hardware.

A Converging Future

Agentic AI is moving from theoretical promise to operational reality. Marktechpost’s 2025 report highlights the industry’s accelerating push to converge language, reasoning, and software interaction into cohesive autonomous systems.

As organizations embed agents across domains—from customer service to supply chain orchestration—the focus will shift toward long-term memory, scalable orchestration, and robust evaluation metrics that go beyond traditional benchmarks. The future of AI will not be scripted—it will be agentic.

Access the Full Report: Download from Marktechpost