Agentic AI consulting from Dedicatted equips you with multi-agent systems that think independently, orchestrate complex workflows, and continuously learn from real-time data—no constant prompting required.
Backed by our expertise in secure cloud architecture and high-quality data integration, we deliver scalable, reliable Agentic AI platforms that boost productivity and ROI from day one.
Agentic AI is transforming manufacturing by enabling autonomous systems to optimize production lines, monitor equipment performance, and adjust operational parameters in real time.Additionally, agentic AI monitors machine health to predict maintenance needs and schedule preventive actions, ensuring minimal downtime
Agentic AI is revolutionizing healthcare by autonomously monitoring medical device data and patient health records to detect anomalies and improve early intervention.In laboratory environments, AI agents automate processes, coordinate test scheduling, and monitor equipment performance, driving efficiency, precision, and reliability across the healthcare ecosystem.
Agentic AI is reshaping the retail landscape by optimizing inventory through real-time monitoring of stock levels, analyzing sales patterns, and automatically adjusting order quantities to meet demand.In supply chain management, agentic AI oversees supplier relationships, tracks shipments, and predicts delays to ensure smooth logistics.
Agentic AI is driving transformation in the finance sector by enhancing risk management through continuous monitoring of transaction patterns and detecting anomalies to prevent fraud and ensure compliance.Additionally, agentic AI optimizes treasury operations by improving cash flow visibility, coordinating payments, and streamlining cash management across financial institutions.
Agentic AI is driving efficiency and safety in the oil and gas industry by continuously tracking equipment performance and predicting maintenance needs to minimize downtime and extend asset life.Additionally, agentic AI tracks environmental indicators, identifies anomalies, and coordinates rapid response actions to ensure compliance and reduce environmental impact.
Dedicatted helps companies design, create, and run useful automated operations that fit into existing DevOps toolchains. Our goal is simple: deliver stable automation that makes releases faster and improves service quality without overburdening operations. This page will give you an overview of how we deal with DevOps agentic AI for real environments, rather than showing off lab demos.
Picking the right resource is extremely important when starting out. When checking out agentic AI platforms for our clients, we see how well they work with source control, CI or CD, observability, and ticketing. We also look for policy controls and other security measures to meet the highest AI security standards. We want to help out clients pick the best resource by giving them a clear breakdown of what the leading agentic AI platforms market has to offer, allowing them to find something that balances the cost and the speed of value. We top things off by making a migration or rollout plan, including guardrails and a clear way to make sure that our customers get the most out of it.
Agentic AI tools are basically software robots that can do more than just produce text, they can actually think. These robots can read text, plan what to do, use tools and perform a variety of tasks. Unlike the usual chatbots that only draft text, agents act. At our company, we control AI agentic workflows and longer agentic workflows in AI that involve code, infrastructure, and service management, with a goal of reducing the amount of work that needs to be done manually. The most important part? Humans will still be there to supervise everything.
If we want to keep our agents useful and safe, we have to build a good structure for them. That’s why we assemble planning and reasoning modules, memory for remembering the state, tools for CI, CD, and cloud, as well as strict policies for actions. This way we can turn ideas into solid agentic AI systems. Our model combines solution design with development, meaning that each sprint improves the safety, expands tool coverage, and proves value with run-book steps that can be easily measured.
Agents repeat the same loop over and over again. The loop starts with them getting a goal, followed by planning the steps, choosing the right tools, evaluating the results, and starting all over until they either succeed or get stopped. The repetition is what enables practical agentic AI use cases like automated incident triage, drift correction, cost checks before deploys, and post-merge validation. Aside from DevOps, we implement agentic AI in healthcare patterns, such as data quality checks, workflow routing, and compliance-friendly summarization, always with approvals and traceability.
Agentic AI refers to an umbrella of AI models that can act independently and autonomously to achieve specified objectives, in contrast to customary AI or generative AI which typically requires an user prompt, to plan, decide and take action within pre-defined constraints. These systems can plan an entire task, then monitor progress, as well as adjust their actions based on feedback from the environment.
Agentic AI is a stronger form of AI, adding the abilities to reason, decide, and execute actions. In addition, it can efficiently call external tools (APIs, databases, and software) to create complex workflows, which makes it more suitable for business processes involving multi-step workflows, conditional rules, and optimization over time.
Agentic AI has become a core capability for smart automation, AI assistants, autonomous customer service agents, workflow orchestration, and decision support systems, as companies move to more advanced systems as part of the digital transformation.
Agentic AI systems typically include:
Autonomy – ability to operate independently within defined boundaries
Goal-oriented behavior – focused on achieving specific objectives
Planning and reasoning – breaking tasks into steps and choosing actions
Tool usage – interacting with APIs, databases, and external systems
Feedback loops – learning from outcomes and adjusting actions
Context awareness – understanding environment, constraints, and priorities
Agentic AI enables organizations to automate complex, multi-step workflows with minimal human involvement. It improves operational efficiency, reduces costs, and allows systems to run continuously without downtime. By making autonomous decisions and adapting based on feedback, agentic AI increases consistency, speeds up execution, and enhances scalability. Businesses benefit from improved productivity, faster response times, and the ability to handle growing workloads without increasing headcount. Agentic AI is especially valuable for intelligent automation, decision support, and end-to-end process orchestration.
Agentic AI systems can also be classified according to the types of agents they use. Single-agent systems are equipped with a single agent for a single task, while multi-agent systems utilize multiple agents for cooperation or coordination. Examples of different agent types include task-specific agents and orchestrator agents easing workflow. Hybrid agentic AI combines the characteristics of independent AI agents with human oversight. Each type of AI offers its own set of advantages for different business and operational needs.
Agentic AI works through a continuous decision-making loop. First, it defines goals and constraints. It then plans actions, executes tasks using tools or systems, observes outcomes, and adapts its strategy based on feedback. This iterative process allows the AI to operate autonomously in dynamic environments. By combining reasoning, execution, and learning, agentic AI can manage complex workflows, respond to changes, and optimize performance without constant human intervention.
Artificial intelligence (AI) can be divided into three large categories based on its capabilities: Customary AI, Generative AI and Agentic AI, which each serve different purposes.
Customary AI includes analysis, prediction, and rule-based decision-making. Customary systems that use logical or statistical rules and algorithms to classify data, detect patterns, and make recommendations are termed artificial intelligence. Customary AI lacks generative capabilities also independent action, requiring direct human input. It gives power to recommendation engines, fraud detection systems, and predictive analytics, among other applications.
Generative artificial intelligence is a class of artificial intelligence models designed to generate various forms of content such as text, images, code, audio, and video through prediction and response to prompts. Generative AI, unlike fully automated systems, requires human input during every stage. It generates content while lacking the autonomy to plan narratives or structure narratives.
Agentic AI is an AI with the ability to autonomously construct plans, use tools, take actions, evaluate their results, and modify its behavior to achieve designated goals. In contrast to generative and predictive AIs, agentic AI can autonomously execute workflows and perform actions in the real world
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