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Freelancer.com CEO Matt Barrie details how "agentic AI" can reliably automate complex, multi-step tasks like performance marketing analysis or processing operational queues. This new capability allows companies to automate entire jobs previously done by teams of people, operating 24/7 at a superhuman level.

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The most impactful AI agent applications are moving beyond simple automation. Composio's CTO uses an agent to perform the full role of a technical recruiter, from sourcing candidates on GitHub to drafting and sending initial outreach emails.

The evolution of 'agentic AI' extends beyond content generation to automating the connective tissue of business operations. Its future value is in initiating workflows that span departments, such as kickstarting creative briefs for marketing, creating product backlogs from feedback, and generating service tickets, streamlining operational handoffs.

The next wave of AI isn't just about single-function tools. It's about agents that act like team members, executing complex, multi-step tasks like competitor research, ad creation, and performance analysis based on a single prompt.

The current pinnacle of the AI stack, 'Agentic AI,' moves beyond simply generating answers to performing autonomous actions. By combining generative models with planning, memory, and tool use (like APIs or code interpreters), these systems can execute complex, multi-step tasks, defining the next wave of product development.

The shift from chatbots to agents represents a jump up the 'use case ladder.' Simple chat focuses on individual generation (drafting emails). In contrast, agents tackle systems-level work like workflow automation and process monitoring, moving AI's value from personal productivity to impacting entire business systems.

Unlike generative AI (like ChatGPT) which only provides text output, agentic AI can perform actions on your behalf. It can log into accounts, click buttons, and complete multi-step tasks, shifting AI from a smart consultant to an autonomous digital assistant.

The 'call and response' nature of large language models (LLMs) is not truly revolutionary for workflows. The significant shift comes from agentic AI, which can connect to various systems and execute multi-step tasks. This moves AI from a content generator to a powerful workflow automation tool.

AI's role has matured from assisting with trivial tasks like homework to autonomously managing complex, multi-step financial and scientific processes end-to-end at major companies like Coinbase, Salesforce, and Novo Nordisk, signaling a fundamental shift in its capability.

The next wave of AI is 'agentic,' meaning it can control a computer to execute commands and complete tasks, not just generate responses to prompts. This profound shift automates workflows like coding and administrative tasks, freeing humans for high-level creative and strategic work.

The next evolution of enterprise AI isn't conversational chatbots but "agentic" systems that act as augmented digital labor. These agents perform complex, multi-step tasks from natural language commands, such as creating a training quiz from a 700-page technical document.