From Code to Intent: The Evolution of AI Agent Testing

Are you experiencing inconsistent results when testing out your new prompt template? One thing I’ve learned: sometimes the instructions aren’t the issue - it’s the model that the prompt template is using. The model can make a big difference in the results you get. So if you’re having a hard time getting the results you’re expecting, try switching the model and see if that improves the output Which is your go-to model for prompt templates? #AI #PromptEngineering #GenerativeAI #LLM #Salesforce #Consulting #Automation

*AI isn’t about bigger models. it’s about solving the right business problem.* - This is my standard Statement as soon as I hear " I want my company to use AI". Most clients jump straight into flashy tools, thinking bigger is better. But the real wins come from: 1. Identifying high-impact pain points 2. Matching AI solutions/ features to actual workflow needs to ease the workload 3. Testing, iterating, and learning/ adapting fast From the experiences of delivering #agentforce solutions I can definitely say Outcome-focused AI beats hype every time. #AIEnablement #salesforce #agentforce

This weekend's project: Creating a Flow Test Case Generator in Salesforce! Utilizing Google's Gemini AI, it automates the process of generating declarative tests for record-triggered flows, greatly reducing manual work. Excited to delve into #SalesforceDeveloper #WeekendProject #Productivity #AI #LWC #CleanCode #Salesforce.

When you hear "AI" what do you think of? Most people imagine chatbots! But there's an important difference between a chatbot and an AI agent. A chatbot reacts. You ask a question, and you get an answer! An AI agent acts. It doesn't just respond, it can also: - Analyzing data from your knowledge base (Knowledge Sources) is an exciting way to learn more about the world around you! - Use tools (search the internet, work with APIs). - Run complex, multi-step processes  - Work in a team with other agents That's why EpicStaff isn't just another bot builder. It's an incredible platform that lets you orchestrate AI agents that work just like real digital employees! 👇 Want to see how it works? Our GitHub is open and we can't wait to see what you come up with! We would absolutely love to have your star! ⭐ [https://lnkd.in/erpqdGVb] #AI #MultiAgent #AIAutomation #DeveloperTools #OpenSource #EpicStaff 

That's why we created EpicStaff. The AI automation market has long been divided into two categories simple but limited no-code builders and powerful, complex frameworks for developers. Our goal is to bridge this gap. We are developing a platform where business users can visually design logic and developers can incorporate Python code. This is true collaboration, not just another chatbot. We look forward to having you among the first users of our open-source community!

Unlock the Future: 6 Tech Layers Powering Smart AI Agents Curious how AI agents are evolving from simple chatbots to “digital employees” that act, remember, and automate? This insightful article from Appetals walks you through the six essential tech stacks enabling such agents—from language models to ethical oversight. 💡 What you’ll discover: The Foundation Layer: LLMs like GPT, Claude, Gemini, Llama—and frameworks like Hugging Face, PyTorch, AWS SageMaker that bring them to life. How the Development, Access, and Context Layers enable agents to integrate with tools, access company data, and remember conversation history. The Orchestration Layer: coordinating multi-agent workflows via tools like Apache Airflow, AutoGen, Kubernetes, etc. The often-overlooked Oversight Layer: safety controls, monitoring, human-in-the-loop governance ensuring agent trustworthiness at scale. Real-world applications—from customer support and marketing to coding and sales—where agents are already delivering dramatic results. If you're building, advising on, or simply curious about intelligent automation, this concise breakdown will refine your strategy and spark fresh ideas. Read the full article here and level up how you think about AI agents: https://lnkd.in/dkDjCQZ9 #AIagents, #TechStack, #AIInnovation

The "𝐫𝐞𝐚𝐥" 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐯𝐚𝐥𝐮𝐞 𝐨𝐟 𝐀𝐈 (𝐀𝐠𝐞𝐧𝐭𝐬) for 𝐂𝐗𝐎𝐬 & 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧-𝐦𝐚𝐤𝐞𝐫𝐬 (Part 1) 💸🤖 . Who doesn't dream of going for a walk in the sun or on the beach while talking to your AI agents on the phone as they earn money for you? 𝐁𝐮𝐭 𝐥𝐞𝐭'𝐬 𝐛𝐞 𝐫𝐞𝐚𝐥... 𝐆𝐞𝐧𝐢𝐞-𝐢𝐧-𝐛𝐨𝐭𝐭𝐥𝐞, 𝐭𝐡𝐚𝐭'𝐬 𝐡𝐨𝐰 𝐈 𝐟𝐞𝐥𝐭 𝐰𝐡𝐞𝐧 𝐈 𝐟𝐢𝐫𝐬𝐭 𝐡𝐞𝐚𝐫𝐝 𝐚𝐛𝐨𝐮𝐭 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬: As a managing director or executive, your job is to deliver results. An AI agent that promises faster, better, and cheaper outcomes sounds like a genie out of a bottle — a real game changer. No wonder everyone wants it... 𝐁𝐮𝐭 𝐀𝐛𝐬𝐞𝐧𝐜𝐞 𝐨𝐟 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐭𝐢𝐚𝐭𝐢𝐨𝐧: But like any new technology, AI has its strengths and weaknesses. Right now, I feel many people are too indifferent about this. Only by understanding both sides can you critically evaluate offers from service providers. That’s why I want to share my experiences so far… Over the past few months, I have spent a lot of time talking to users and CEOs about AI (agents) and conducting my own experiments. In the process, a few patterns have emerged for me. 𝐌𝐲 𝐥𝐞𝐬𝐬𝐨𝐧𝐬 𝐈 𝐥𝐞𝐚𝐫𝐧𝐞𝐝, 𝐩𝐚𝐫𝐭 1: The logic behind AI is statistical guessing based on historical data sets. This leads to suitable and unsuitable tasks. 𝐀𝐩𝐩𝐫𝐨𝐩𝐫𝐢𝐚𝐭𝐞 𝐭𝐚𝐬𝐤𝐬: + AI is very good at creative tasks (if you provide a structure or framework) , text creation, image creation, and video creation, as long as no larger context needs to be consistently maintained. + The more public training material available on this topic, the better AI is generally suited to it. +This applies in particular to AI programming support, which is one of the low-hanging fruits for good staff thanks to various code plugins. +N8N combined with AI offers great potential for process automation in combination with AI agents. 𝐍𝐨𝐧-𝐬𝐮𝐢𝐭𝐚𝐛𝐥𝐞 𝐭𝐚𝐬𝐤𝐬: +/- AI amplifies competence: skilled people achieve better results faster, while unskilled ones risk creating scalable errors and problems -Most AI solutions I’ve seen so far can’t deliver high-quality results at scale without experts. The outputs lack consistency. Combining them with classic process- and logic-based automation can help. -The data quality for most is too poor, i.e., lacking structure for their own AI agents. 𝐏𝐒: This is Part 1 of my AI series, where I share insights from my own experience to bring more real value to LinkedIn. If you find it helpful, leave a like or comment so I know you're interested. I’ll be posting weekly about practical AI experiences for managers – follow me to stay tuned. 𝐏𝐏𝐒: What is your experience, what works well, what works poorly? In your experience, what are the low-hanging fruits of AI? Where do you see the most attractive business cases?

For Agentic AI users, its important to know what to check when your framework mis behaves with inconsistencies. Some examples are shown here...

Human-in-the-Loop and LangSmith: The Hidden Pillars of Reliable AI Agents As I continue learning about AI agents, two concepts stand out for their importance in making these systems trustworthy: Human-in-the-Loop (HITL) and LangSmith. 1. Human-in-the-Loop (HITL) This means humans stay involved at key decision points in an AI workflow. Instead of letting the agent act fully autonomously, checkpoints are added where people can review, approve, or correct outputs. In organizations: Used in healthcare (doctors validating AI recommendations), finance (compliance teams reviewing flagged transactions), and customer support (agents stepping in when AI gets stuck). In daily life: Think about Gmail’s smart reply AI suggests, but you choose the final send. Or ride-sharing apps where drivers validate route changes instead of leaving it all to the algorithm. 2. LangSmith LangSmith is about observability and debugging for AI workflows. It gives developers transparency into how agents reason step by step, making it possible to test, trace, and continuously improve their performance. In organizations: Teams use it to monitor chatbots, RAG systems, and AI assistants to ensure accuracy and compliance at scale. In daily life: Every time an app gets “smarter” with better recommendations or fewer mistakes, chances are teams are using tools like LangSmith behind the scenes to debug and refine models. Why They Matter Together HITL keeps AI grounded with human judgment. LangSmith ensures AI isn’t a black box but a system developers can monitor, improve, and trust. My takeaway: AI agents don’t just work because of models,they work because of oversight (HITL) and visibility (LangSmith). Organizations rely on them to build production-grade systems, and everyday users benefit from them often without realizing it.

Salesforce just cut 4,000 support staff as AI agents take over. This isn’t the future of work. it’s already here. Traditional CRMs are fast losing their value. With AI handling the intelligence, all that really matters is a reliable database to power it. This will likely change the foundation of how businesses think about tech stacks. What’s your take? Will CRMs become obsolete? Personally, I believe it's just a matter of time. Source: https://lnkd.in/gyyBNX3q #AI #TechTrends #CustomerService #Salesforce #JobDisruption