Implementation of Frameworks

When working with Agentic AI, selecting the right framework is crucial. Each one brings different strengths depending on your project needs — from modular agent designs to large-scale enterprise security. Here's a structured breakdown: ➔ 𝗔𝗗𝗞 (𝗚𝗼𝗼𝗴𝗹𝗲) • Features: Flexible, modular framework for AI agents with Gemini support • Advantages: Rich tool ecosystem, flexible orchestration • Applications: Conversational AI, complex autonomous systems ➔ 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵 • Features: Stateful workflows, graph-based execution, human-in-the-loop • Advantages: Dynamic workflows, complex stateful AI, enhanced traceability • Applications: Interactive storytelling, decision-making systems ➔ 𝗖𝗿𝗲𝘄𝗔𝗜 • Features: Role-based agents, dynamic task planning, conflict resolution • Advantages: Scalable teams, collaborative AI, decision optimization • Applications: Project simulations, business strategy, healthcare coordination ➔ 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗦𝗲𝗺𝗮𝗻𝘁𝗶𝗰 𝗞𝗲𝗿𝗻𝗲𝗹 • Features: AI SDK integration, security, memory & embeddings • Advantages: Enterprise-grade security, scalable architecture • Applications: Enterprise apps, workflow automation ➔ 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗔𝘂𝘁𝗼𝗚𝗲𝗻 • Features: Multi-agent conversations, context management, custom roles • Advantages: Simplifies multi-agent orchestration, robust error handling • Applications: Advanced chatbots, task planning, AI research ➔ 𝗦𝗺𝗼𝗹𝗔𝗴𝗲𝗻𝘁𝘀 • Features: Lightweight, modular multi-agent framework • Advantages: Low-compute overhead, seamless integration • Applications: Research assistants, data analysis, AI workflows ➔ 𝗔𝘂𝘁𝗼𝗚𝗣𝗧 • Features: Goal-oriented task execution, adaptive learning • Advantages: Self-improving, scalable, minimal human intervention • Applications: Content creation, task automation, predictive analysis    Choosing the right Agentic AI framework is less about the "most powerful" and more about 𝗺𝗮𝘁𝗰𝗵𝗶𝗻𝗴 𝘁𝗵𝗲 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸’𝘀 𝗰𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀 𝘁𝗼 𝘆𝗼𝘂𝗿 𝗽𝗿𝗼𝗷𝗲𝗰𝘁'𝘀 𝗰𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝘆, 𝘀𝗰𝗮𝗹𝗲, 𝗮𝗻𝗱 𝗴𝗼𝗮𝗹𝘀. → Which one have you used or are excited to try? → Did I miss any emerging frameworks that deserve attention?

🛑 𝐒𝐓𝐎𝐏 𝐛𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭𝐬 𝐟𝐫𝐨𝐦 𝐬𝐜𝐫𝐚𝐭𝐜𝐡. Instead use this repository: 40+ production-ready agent implementations with complete source code, from basic conversational bots to enterprise multi-agent systems. 𝐖𝐡𝐚𝐭 𝐜𝐚𝐮𝐠𝐡𝐭 𝐦𝐲 𝐚𝐭𝐭𝐞𝐧𝐭𝐢𝐨𝐧: ↳ LangGraph AI workflows with state management examples ↳ Self-healing code agents that debug themselves ↳ Multi-agent research teams using AutoGen ↳ Memory-enhanced systems with episodic + semantic storage ↳ Advanced RAG with controllable retrieval strategies 𝐓𝐡𝐞 𝐭𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐝𝐞𝐩𝐭𝐡 𝐢𝐬 𝐢𝐦𝐩𝐫𝐞𝐬𝐬𝐢𝐯𝐞: ↳ Vector embeddings with Pinecone/ ChromaDB integration ↳ Async processing patterns for concurrent agent execution ↳ Pydantic models for structured agent outputs ↳ Real-world error handling and retry mechanisms Each implementation includes: ✅ Complete notebooks with explanations ✅ Architecture diagrams and workflow logic ✅ Integration patterns for popular frameworks ✅ Performance optimization techniques This is essentially a master class in agent engineering disguised as a GitHub repo by Nir Diamant. Perfect for AI engineers who want to understand how these systems work and where to get started. 🔗 Repository: https://lnkd.in/dmGE-t_6 Which agent architecture are you most curious about? The multi-agent collaboration patterns are fascinating. ♻️ If you found this useful: I regularly share Cloud & AI insights(through my newsletter subscribe https://lnkd.in/dRifnnex) hit follow (Priyanka Vergadia) and feel free to share it so others can learn too! #AIEngineering #LangChain #LangGraph #MultiAgent #MachineLearning #RAG #VectorDB #OpenAI #Ai #AIEngineer #AIAgents #agenticai

If you’re an AI engineer building a full-stack GenAI application, this one’s for you. The open agentic stack has evolved. It’s no longer just about choosing the “best” foundation model. It’s about designing an interoperable pipeline, from serving to safety- that can scale, adapt, and ship. Let’s break it down 👇 🧠 1. Foundation Models Start with open, performant base models. → LLaMA 4 Maverick, Mistral‑Next‑22B, Qwen 3 Fusion, DeepSeek‑Coder 33B These models offer high capability-per-dollar and robust support for multi-turn reasoning, tool use, and fine-grained control. ⚙️ 2. Serving & Fine-Tuning You can’t scale without efficient inference. → vLLM, Text Generation Inference, BentoML for blazing-fast throughput → LoRA (PEFT) and Ollama for cost-effective fine-tuning If you’re not using adapter-based fine-tuning in 2025, you’re overpaying and underperforming. 🧩 3. Memory & Retrieval RAG isn’t enough, you need persistent agent memory. → Mem0, Weaviate, LanceDB, Qdrant support both vector retrieval and structured memory → Tools like Marqo and Qdrant simplify dense+metadata retrieval at scale → Model Context Protocol (MCP) is quickly becoming the new memory-sharing standard 🤖 4. Orchestration & Agent Frameworks Multi-agent systems are moving from research to production. → LangGraph = workflow-level control → AutoGen = goal-driven multi-agent conversations → CrewAI = role-based task delegation → Flowise + OpenDevin for visual, developer-friendly pipelines Pick based on agent complexity and latency budget, not popularity. 🛡️ 5. Evaluation & Safety Don’t ship without it. → AgentBench 2025, RAGAS, TruLens for benchmark-grade evals → PromptGuard 2, Zeno for dynamic prompt defense and human-in-the-loop observability → Safety-first isn’t optional, it’s operationally essential 👩💻 My Two Cents for AI Engineers: If you’re assembling your GenAI stack, here’s what I recommend: ✅ Start with open models like Qwen3 or DeepSeek R1, not just for cost, but because you’ll want to fine-tune and debug them freely ✅ Use vLLM or TGI for inference, and plug in LoRA adapters for rapid iteration ✅ Integrate Mem0 or Zep as your long-term memory layer and implement MCP to allow agents to share memory contextually ✅ Choose LangGraph for orchestration if you’re building structured flows; go with AutoGen or CrewAI for more autonomous agent behavior ✅ Evaluate everything, use AgentBench for capability, RAGAS for RAG quality, and PromptGuard2 for runtime security The stack is mature. The tools are open. The workflows are real. This is the best time to go from prototype to production. ----- Share this with your network ♻️ I write deep-dive blogs on Substack, follow along :) https://lnkd.in/dpBNr6Jg

We tell our sales reps to be gritty, to work smarter, not harder, to smash their quota but don't always do the best job pairing those inspirational calls to action with tools and techniques that allow them to do the things we ask. For years, I’ve loved the GROW Goal Setting Model. It is a great model, but I found myself tweaking it to reflect the things I think are fascinating and that actually work for revenue teams. 🧠 Ideas like: - Neuroplasticity - Harms of moonshot thinking - Value of gratitude and meditation - The frustration reps feel when they work tirelessly and still miss quota. That’s why I developed the PATH. 👉 Steal this framework to help your team not only set goals but achieve them. The PATH framework is a four-step process that helps you and your team set actionable goals, anticipate challenges, and ensure every step aligns with your aspirations. 1. Plan: Setting a Focused Goal Everything starts with a solid foundation. The first step is setting a focused goal. SMART goals—Specific, Measurable, Achievable, Relevant, and Time-bound—work well here. This ensures you’re working toward a well-defined target, making it easier to stay focused and track progress. 2. Anticipate: Backcasting Once your goal is in place, it’s time to imagine your desired future state. I love writing goals as if they've already happened and writing out the details of what it took me to get there. This process ensures that you have realistic micro-actions that you can be accountable to on the PATH to achieving your goal. 3. Test: Pre-Mortem Next, you stress-test your plan with a pre-mortem (inspo credit: Annie Duke, Thinking in Bets) This exercise allows you to identify risks before they arise, so you can adjust your plan and stay on track. It also encourages you to uncover opportunities to leapfrog your progress by brainstorming creative solutions. 4. Harmonize: Alignment to Aspirations The final step ensures that your micro-actions align with your larger aspirations. It's a final sense check to ensure you've set a goal you care enough about that you'll put in the hard work required to achieve it. That work will be supported by a clear PATH to success. The PATH framework ensures you don’t just set goals—you achieve them. 💸 Want me to guide your sales or leadership team through this process as part of your year-end planning or SKO? Drop "PATH" in the comments to learn more.

I was recently reminded of the significant grounding power of Prosci's five tenets of #changemanagement and the associated plain language questions that can unlock obstacles and provide focus. When I am on stage to share the mindset that people are indeed on the critical path of #changesuccess with a group that may not know much about change management (i.e. executives and senior leaders), this is one of the frameworks I lean on regularly. Why? The first three tenets do not mention change management, but describe the nature of change and how it happens. Then, in the fourth tenet, change management is positioned as the solution to the realities of successful change (or as an antidote when change has been left to chance). Tenet five closes the loop by connecting adoption back to the project's purpose. Prosci's Five Tenets of Change Management + Plain Language Questions: Tenet 1: We change for a reason. Question 1:Why are we changing? T2: Organizational change requires individual change. Q2: Who has to do their job differently (and how)? T3: Organizational outcomes are the collective result of individual change. Q3: How much of our outcomes depend on adoption and usage? T4: Change management is an enabling framework for managing the people side of change. Q4: What will we do to support adoption and usage? T5: We apply change management to realize the benefits and desired outcomes of change. Q5: How will driving adoption and usage improve results? When I'm engaging senior leaders, I usually lead with the five question and only concluded with the connection back to the discipline of change management. How have you used the five tenets framework in your work?

According to the Harvard Business Review, 70% of change initiatives fail. 👇 Leading change isn’t a one-size-fits-all job. It’s a mix of psychology, process, and culture. That’s why smart leaders don’t just use one framework—they use the right one for the moment. Here are 5 proven models and when to use each: 𝗠𝗮𝘂𝗿𝗲𝗿’𝘀 𝟯 𝗟𝗲𝘃𝗲𝗹𝘀 𝗼𝗳 𝗥𝗲𝘀𝗶𝘀𝘁𝗮𝗻𝗰𝗲 ---> Use when facing emotional or personal pushback. Tackles: “I don’t get it,” “I don’t like it,” “I don’t trust you.” 𝗟𝗮𝗠𝗮𝗿𝘀𝗵 𝗠𝗼𝗱𝗲𝗹 ---> Great for managing risk. Focuses on identifying + mitigating adoption barriers before they block progress. 𝗣𝗗𝗦𝗔 𝗖𝘆𝗰𝗹𝗲 ---> Perfect for product or process iteration. Test → Learn → Adapt → Repeat. Works wonders in agile environments. 𝗞𝘂𝗿𝘁 𝗟𝗲𝘄𝗶𝗻’𝘀 𝗠𝗼𝗱𝗲𝗹 ---> Change = Unfreeze → Transition → Refreeze. Simple, powerful, and ideal for company-wide shifts. 𝗔𝗗𝗞𝗔𝗥 𝗠𝗼𝗱𝗲𝗹 ---> Best for individual behavior change. Awareness, Desire, Knowledge, Ability, Reinforcement. Every step matters. There’s no “best” model. Only the one that fits your team, timing, and terrain. Which of these do you use most? -- Follow Alan (AJ) Silber for frameworks that make strategy simple.

The 𝗔𝗜 𝗗𝗮𝘁𝗮 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 guidance from 𝗗𝗛𝗦/𝗡𝗦𝗔/𝗙𝗕𝗜 outlines best practices for securing data used in AI systems. Federal CISOs should focus on implementing a comprehensive data security framework that aligns with these recommendations. Below are the suggested steps to take, along with a schedule for implementation. 𝗠𝗮𝗷𝗼𝗿 𝗦𝘁𝗲𝗽𝘀 𝗳𝗼𝗿 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 1. Establish Governance Framework     - Define AI security policies based on DHS/CISA guidance.     - Assign roles for AI data governance and conduct risk assessments.  2. Enhance Data Integrity     - Track data provenance using cryptographically signed logs.     - Verify AI training and operational data sources.     - Implement quantum-resistant digital signatures for authentication.  3. Secure Storage & Transmission     - Apply AES-256 encryption for data security.     - Ensure compliance with NIST FIPS 140-3 standards.     - Implement Zero Trust architecture for access control.  4. Mitigate Data Poisoning Risks     - Require certification from data providers and audit datasets.     - Deploy anomaly detection to identify adversarial threats.  5. Monitor Data Drift & Security Validation     - Establish automated monitoring systems.     - Conduct ongoing AI risk assessments.     - Implement retraining processes to counter data drift.  𝗦𝗰𝗵𝗲𝗱𝘂𝗹𝗲 𝗳𝗼𝗿 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻  Phase 1 (Month 1-3): Governance & Risk Assessment   • Define policies, assign roles, and initiate compliance tracking.   Phase 2 (Month 4-6): Secure Infrastructure   • Deploy encryption and access controls.   • Conduct security audits on AI models. Phase 3 (Month 7-9): Active Threat Monitoring • Implement continuous monitoring for AI data integrity.   • Set up automated alerts for security breaches.   Phase 4 (Month 10-12): Ongoing Assessment & Compliance   • Conduct quarterly audits and risk assessments.   • Validate security effectiveness using industry frameworks.  𝗞𝗲𝘆 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 𝗙𝗮𝗰𝘁𝗼𝗿𝘀   • Collaboration: Align with Federal AI security teams.   • Training: Conduct AI cybersecurity education.   • Incident Response: Develop breach handling protocols.   • Regulatory Compliance: Adapt security measures to evolving policies.  

Why your change efforts fail—and how to fix them. Uncover hidden obstacles that sabotage your best plans. Change is hard, but it doesn’t have to be chaotic. The Knoster Model breaks down the essential components for successful change. It’s a simple yet powerful framework that highlights what can go wrong when a key element is missing. Let’s break it down: Vision ↳ Without a clear vision, you’re setting yourself up for confusion. ↳ People need to know where they’re headed. Skills ↳ Lacking the necessary skills leads to anxiety.      ↳ Training and development are critical. Incentives ↳ No incentives? Expect resistance. ↳ People need to see the value in the change. Resources ↳ Missing resources create frustration.  ↳ Make sure teams have what they need to succeed. Action Plan ↳ An unclear action plan results in false starts. ↳ A well-thought-out plan is the backbone of execution. Success isn’t just about having the right pieces—it’s about ensuring none of them are missing. Has your team faced these obstacles during change? Share your experiences to help others navigate similar challenges. ⬇️ ♻️ Found this helpful? Share to spread the word about effective change management. Follow Tom Wanek for more strategies on leading successful change initiatives.

Building a Strong Foundation: How to Create an Effective Organizational Profile with NIST CSF 2.0 🔐💼 Creating a solid cybersecurity strategy starts with understanding where your organization currently stands. The NIST Cybersecurity Framework (CSF) 2.0 offers a structured way to evaluate and strengthen your security practices. One of the most important steps is developing an Organizational Profile—a tool that helps you map out your existing controls, identify gaps, and plan improvements. This guide will walk you through the process of building an Organizational Profile, so you can take meaningful steps toward enhancing your organization’s security. 1. Define the Scope: Determine the specific systems, processes, or threats the profile will address. For instance, it could encompass the entire organization, financial systems, or ransomware-specific responses. Multiple profiles can be created to target different areas or objectives. 2. Collect Relevant Data: Gather information such as organizational policies, cybersecurity standards, risk management goals, BIAs (Business Impact Analyses), enterprise risk assessments, and existing tools or practices. These details form the foundation of the profile. 3. Build the Profile: Using the collected data, document your organization’s alignment with CSF outcomes. Highlight current strengths and risks. This step establishes your Current Profile, which serves as the baseline for future improvements. Community Profiles can be a helpful reference when planning your Target Profile. 4. Conduct a Gap Analysis: Compare the Current Profile to the desired Target Profile. Identify gaps and prioritize improvements. Use tools like a risk register or POA&M (Plan of Action and Milestones) to effectively develop an actionable plan to address these gaps. 5. Execute and Update: Implement the action plan to close identified gaps and improve alignment with the Target Profile. Continuously monitor and update the profile to reflect organizational changes and evolving threats. By creating an Organizational Profile using the NIST CSF 2.0 framework, organizations can assess their current security posture and take deliberate steps to enhance their resilience. This ongoing process ensures that as threats evolve, so does your organization’s ability to address them. How is your organization aligning with the NIST CSF 2.0? #Cybersecurity #NISTCSF #RiskManagement #CyberResilience #OrganizationalProfile #NISTCSF2.0 #SecurityStrategy #CyberAwareness #InformationSecurity #RiskAssessment

Ever been handed a vague project like "We need better personas" and a crazy deadline? A simple framework can turn that chaos into clear action: The key? Start with the END GOAL in mind and work backwards. This is because only when you’re clear on the outcome can you create a process that’s realistic, effective, and aligned with business goals. Let’s break it down with the example: "We need better personas." 🎯 Step 1: Define the end goal Ask: Why do we need better personas? What’s the real business metric we’re trying to move? Example: Increase win rates by 9% over the next 6 months. In this case, it’s clear the project isn’t just about creating personas, it’s about using those personas to sharpen messaging and drive more sales. 🎯 Step 2: Align stakeholders & set milestones Before jumping into deliverables, align with key stakeholders. Ensure everyone agrees on the goals, timelines, and success metrics. Kickoff meeting: Confirm the end goal, scope, and key deliverables. Milestone check-ins: Schedule  updates to ensure alignment and course-correct if needed. 🎯 Step 3: Get specific on deliverables If the focus is on increasing win rates, what’s needed beyond just personas? - > Persona profiles: Core buyer personas, pain points, triggers, buying journey maps, and content preferences. - > Messaging guide: Value propositions, key messaging themes with proof points, objection handling, and specific talking points. - > Sales enablement toolkit: Persona-specific pitch decks, talk tracks, one-pagers, FAQs, and objection-handling guides. 🎯 Step 4: Gather data Given the timeline and goals, what’s realistic for research? Examples could be: - > Deploy a customer survey to 200 customers to refine and segment personas. - > Analyze 10 closed sales deals within ICP. - > Conduct 5 in-depth customer interviews for qualitative insights. 🎯 Step 5: Build, test, and iterate Once stakeholders agree on the research plan and deliverables, start building and validating. - > Develop personas and associated messaging. - > A/B test messaging to validate impact (e.g. using emails) -> Collect sales team feedback on persona usability and messaging effectiveness. Key takeaway: Working backwards forces clarity and also makes it easier for you to counter unrealistic times.  I have been working through this process with dozens of clients to help them get more clarity. I’d love to hear from you! How do you approach vague project requests? #productmarketing #coaching #GTM #productivity #career