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In just a few clicks, generate marketing content by automating every step with specialized AI agents, from planning topics to generating SEO-optimized articles, right through to posting them on your sites.
Create thousands of pages of content rapidly while maintaining optimum quality, thanks to simultaneous workflows driven by AI agents.
An intuitive, ready-to-use application, enabling your teams to collaborate and automate complex tasks without having to interact directly with the AI.
Total Energies was looking to optimize the updating of its web content, a costly and time- consuming manual process that was affecting its SEO and audience. Draft&Goal proposed a solution based on Al agents to automate this process. This enabled TotalEnergies to simplify content updates, significantly increase efficiency and maintain optimal SEO performance, while freeing up resources for higher value-added tasks.
Faced with the need to generate tens of thousands of unique web pages to improve its SEO, Turo was faced with a challenge of scale that was impossible to manage manually. With Draft&Goal, Turo automated page generation from databases and templates, creating unique pages in a matter of hours. This solution enabled Turo to instantly increase its organic traffic, while reducing content production costs to just $1 per page.
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Ai Automation
Agentic workflows represent a new paradigm in the automation of complex business processes. By leveraging intelligent agents, businesses can optimize their workflows to handle complex tasks that traditional systems are often unable to manage efficiently. The true strength of an agentic workflow lies in its ability to integrate autonomous agents that are capable of executing tasks independently, making decisions in real time, and collaborating seamlessly with other agents within the system.
Each agent within an agentic workflow is built to perform a set of predefined tasks, utilizing language models and artificial intelligence to interpret content, analyze data, and adapt its behavior based on new inputs. This capability is particularly valuable in business settings that require handling diverse processes such as problem solving, data management, and multi-agent collaboration. By employing multi-agent systems, agentic workflows enable a level of flexibility and autonomy that surpasses traditional, static workflow models.
Advanced Agent Functionnalities
Large language models (LLMs) and agent-based systems are revolutionizing business operations, transforming traditional problem-solving and task execution. These models are not limited to basic, command-driven processes; instead, they leverage advanced language models and contextual understanding to execute complex workflows. By integrating multi-agent systems, businesses can create agentic workflows where agents collaborate in real time, using natural language prompts to communicate and synchronize their actions.
Agentic workflows empower autonomous agents to handle intricate tasks by processing vast amounts of data through sophisticated language models. This approach enables each agent to perform distinct roles within the workflow, contributing unique capabilities such as decision making, data interpretation, and adaptive task management. With LLM-based agentic systems, businesses can build multi-agent frameworks that not only automate routine processes but also manage complex tasks that require nuanced problem solving. This versatility stems from the LLMs’ ability to comprehend language inputs, generate structured content, and adapt dynamically to evolving business scenarios.
Model Agnostic
The foundation of agentic workflows is built on a powerful synergy between open-source tools and advanced large language models (LLMs). This combination is pivotal in enabling businesses to design highly adaptive agentic systems that can manage complex workflows and address intricate tasks with efficiency. By leveraging the flexibility of open-source frameworks, organizations can build agents that are not only tailored to their specific needs but also capable of handling complex tasks that traditional systems struggle to execute.
Choosing an open-source approach for developing agentic workflows ensures unparalleled flexibility, scalability, and continuous improvement as technology and artificial intelligence evolve. Open-source design patterns enable businesses to maintain complete control over their agentic systems without being confined by proprietary system limitations. This autonomy is critical when building multi-agent systems designed for complex workflows that demand high levels of customization.
Custom made for you
Creating agents that fit your unique business requirements lies at the core of efficient agentic workflow design. The first step to building agents is to identify the specific types of tasks and processes that would most benefit from automation. For many organizations, these tasks can range from repetitive content generation, detailed data analysis, or even supporting complex problem-solving through multi-agent collaboration and decision-making scenarios. By mapping out these processes and understanding the requirements, you can strategically implement agentic workflows that align with your goals. From there, the design patterns for each agent should be developed to define how they interact with other agents within the system. This ensures a seamless integration into existing business workflows and allows for precise task execution. Leveraging advanced tools like prompt engineering and large language models (LLMs), organizations can build agents that not only understand complex language but also adapt to new tasks based on real-time inputs and data-driven insights. By incorporating these language models, agents gain the ability to handle complex tasks such as long-term memory processing, dynamic content creation, and in-depth data extraction. Additionally, the use of multi-agent systems supports real-time collaboration, where multiple agents coordinate to achieve high-level business outcomes. With open-source frameworks and artificial intelligence, building agents becomes a streamlined process that facilitates scalability and flexibility.
Create Content at Scale
Agentic workflows are highly effective in intelligent decision-making because they leverage complex systems designed to automate intricate decision trees and logic-based processes, making them a powerful tool for businesses seeking to optimize operations. By integrating multi-agent systems, agentic workflows allow agents to collaborate seamlessly, dynamically adjusting their roles and strategies in response to changing business needs, real-time data, and evolving parameters. This adaptability is crucial in complex workflows where quick and accurate decision-making is paramount, particularly in environments that require autonomous agents to perform under fluctuating conditions. The use of large language models (LLMs) further amplifies the capabilities of these agents, enabling them to interpret natural language inputs, understand context, and respond with precision. Through sophisticated prompt engineering and integration of machine learning, agents within agentic workflows can process and analyze vast data sets, providing real-time insights and supporting long-term memory functions. This ensures that agentic systems maintain high performance and reliability, even in multi-agent collaboration scenarios, where various agents work together to achieve strategic business outcomes. By building agents that operate within these workflows, businesses can automate everything from simple tasks to complex tasks that demand a deep understanding of context and high-level reasoning.
Privacy Focused
An effective agentic system must include a comprehensive suite of monitoring tools designed to track agent performance, data flow, and workflow execution in real time. These tools are essential in agentic workflows, especially when managing multi-agent systems that rely on seamless communication and coordination between agents. By continuously analyzing workflow processes, businesses can detect issues early, ensure optimal task allocation, and make agile adjustments based on real-time data insights. The implementation of such monitoring tools is critical in complex workflows where autonomous agents handle multiple interconnected tasks that demand constant synchronization and decision-making. Real-time monitoring within agentic systems enhances problem solving by enabling agents to dynamically adapt their behavior based on incoming data, system conditions, and changing business requirements. .
