Agentic Ai

You have ServiceNow running your ITSM. Your team fields 15,000 tickets a month. Change requests pile up waiting for approvals that could be automated. Incident resolution that should take 20 minutes takes 3 hours because three systems don’t talk to each other. ServiceNow is telling you agentic AI solves this. So is every other vendor right now. The question is whether ServiceNow’s agentic AI delivers autonomous action or just smarter autocomplete. This guide breaks down the architecture, real ROI numbers, and the friction points most vendor demos skip. ...

Your infrastructure runs on AWS. Your team has approval to build agentic AI. Now you face the question that takes most engineering leads three weeks to answer confidently: is Amazon Bedrock Agents the right foundation, or will the architecture choices you make now lock you into a path that is hard to reverse? AWS holds 31% of global cloud market share (Synergy Research, Q4 2024). For the majority of enterprise engineering teams, that means your production systems – Lambda, RDS, S3, API Gateway – already live in Bedrock’s native ecosystem. The question is not whether AWS has a competitive offering. It is whether the platform’s tradeoffs map to what your team actually needs to build. ...

Your team has approved a budget for agentic AI. Your infrastructure runs on Google Cloud. Now comes the question that engineers and CTOs spend weeks trying to answer: is Vertex AI Agent Builder the right place to build, or are you locking into a Google-shaped box that limits you later? This guide gives you a straight answer – what the platform actually does, where it outperforms alternatives, and where the friction is real. ...

Generative AI vs Agentic AI: The Difference That Changes Everything Companies are spending $30,000 to $150,000 on agentic AI systems for problems that a $50/month generative AI API subscription would have solved. The reverse happens too: teams settle for a ChatGPT wrapper on a process that runs 500 times a day with 12 external system dependencies – and wonder why it never scales. The gap between generative AI and agentic AI is specific and technical. It is also consistently misrepresented. According to Gartner, 33% of enterprise software applications will include agentic AI by 2028, up from less than 1% in 2024 – but a significant portion of current “agentic AI” pitches are generative AI with a better interface. Buying the wrong category costs you either budget or months of engineering time. ...

Your finance team spends 12 hours weekly routing invoices between systems. Your support team manually triages 200 tickets daily. Your sales ops person builds the same reports every Monday morning. Traditional automation helped - you saved maybe 20-30% of that time with Zapier workflows and if-then rules. But the workflows still break when something unexpected happens. They still need constant maintenance. They still can’t think. That changes with agentic AI workflow automation. ...

The AI landscape has shifted dramatically since 2022, and two terms dominate enterprise conversations: agentic AI and generative AI. While they’re often used interchangeably, they represent fundamentally distinct approaches to artificial intelligence - each with unique capabilities, use cases, and business implications. Agentic AI refers to autonomous systems that can take actions, make decisions, and complete complex tasks with minimal human intervention. Generative AI, on the other hand, focuses on creating new content - text, images, code, or media - based on learned patterns from training data. ...