How Mem0 makes AI agents understand time and memory

AI can automate — but without transparency, it can’t be trusted. Discover how observability turns AI’s decisions into explainable, auditable actions that teams can truly rely on. 👉https://lnkd.in/g2wGg-Ey

Hot Take Wednesday! AI won’t replace AE's / CSMs. But it will replace AE's / CSMs who don’t use it. Future CSM dashboards will say: ‘Here are your 5 riskiest accounts and why.’ That’s not replacing humans — that’s giving them superpowers. 👉 Agree or disagree?”

Ever had your AI completely break after a model update? One day it works fine, the next it refuses tasks or gives totally different answers. Frustrating, right? That’s because too many rules and workflows are buried inside one giant prompt. When the model shifts, your whole system collapses. The fix is simple: Separate the job from the model. Write a clear spec and evaluation tests so the task stays constant. Put tools and control flow in code. That way you can easily swap in a new model or inference layer without chaos. Treat models as replaceable parts, not the entire system. superstition ≠ system. A solid setup doesn’t fear updates. And for accountability, give your agent a verifiable .web3 identity, premium names are going fast.

Stop tuning prompts. Start shipping tests. If your agent can’t be replayed, it can’t be trusted. MCP turns tools/data/workflows into a stable, auditable interface—so you can: - Record/replay full agent runs across Claude/GPT/Llama - Regression-test actions with deterministic tool schemas - Track cost/latency/safety per step in CI, not prod Would agent CI be a hard gate at your org? Why or why not? Follow for a practical MCP replay checklist next. #ModelContextProtocol #AI #Agents #MLOps #OpenStandards #Enterprise

AI search is rewriting the rules. Are you ready to compete when clicks disappear and assistants call the shots? 🔎 AI-powered discovery is now the norm. Outlets like Analytics Insight and Little Black Book | LBBOnline explain that entity-rich, well-structured content wins visibility in AI Overviews and assistant answers. 🤖 Agent-friendly structure matters. As PhocusWire and Pulse 2.2950 detail, brands moving to machine-readable data and live integrations stand out where AI and agents transact or retrieve info directly. 🛑 AI crawler policies are essential. Financial Times highlights legal pushback on AI data scraping, showing proactive governance like llms.txt is now critical for protecting your assets. Anable puts your AI readiness on autopilot with detection for AI visibility issues, agent-friendly scores, llms.txt checks, and performance insights so your brand earns presence across AI search surfaces. Discover your AI Readiness Score at www.anable.ai and connect to accelerate your site's future. #AI #SEO #DigitalTransformation #Innovation #ArtificialIntelligence

AI Agents 101: The 5 Core Components Here's what powers every AI Agent: ✓ LLM as the Brain | The reasoning engine that makes the agent autonomous ✓ Instructions | Defines role, behavior, and boundaries ✓ Tools | Enables real actions (APIs, databases, email, CRMs) ✓ Memory | Remembers context about your company ✓ Knowledge (RAG) | Your proprietary company data The combination of these 5 components transforms an Agent System into a digital employee that is suited to your company, can thinks remembers, and executes pre-defined tasks.

The biggest mistake I see is expecting AI to “do everything.” Results come from clarity and guardrails, not magic. Your prompts are product requirements. If they’re vague, the output will be too. And human verification isn’t optional; it’s how you close the last mile. A simple checklist helps: • Define a narrow use case with a specific scenario and outcome. • Write precise prompts with examples and edge cases. • Keep a human in the loop for review where impact is high. • Invest in clean historical data and useful knowledge articles. • Measure quality with a baseline before you automate. Which single item on this list would move the needle most for you right now?

AI search is changing fast. This week’s takeaways from Search Engine Land show a new playbook emerging for leaders who want real visibility with AI users and agents. - AI systems now favor clear, brand-owned content over crowd forums like Reddit 📊 - Agent-friendly pages with concise answers and structured data boost AI selection odds - Core Web Vitals and tools like llms.txt shape crawlability and reliable AI visibility Anable checks your AI Readiness Score, spots llms.txt, and tests agent-friendly structure so your brand leads in this new landscape. See how prepared your site is at www.anable.ai #AIReadiness #AISEO #AgentFriendly #StructuredData #CoreWebVitals

There’s still this hilarious myth floating around that Prompt Engineers know secret phrases that can make your LLMs 10x more accurate. Reality check: most serious GenAI systems aren’t built on “magic words.” They’re built on dynamic prompts, orchestration logic, structured data, and components that all need to work together. I’ve had clients ask me to “fix their prompts.” But you don’t fix a prompt. You fix the system — the agent logic, the data pipeline, the context handling, and the noise filtering. It’s all connected. With LLMs, less is more. The challenge isn’t writing long prompts or stacking sub-agents. It’s designing clean, context-aware instructions that trigger only when needed and still handle every scenario. If your team is still following every prompt-writing “guideline” blindly, the problem isn’t GenAI. It’s how you’re using it. --------------------- AI is not intelligent if used stupidly. #mindchords_ai

From Models to Systems: the unglamorous work that makes AI useful Most AI programs don’t stall on modeling. They stall on systems thinking. The hard part isn’t getting an LLM to answer; it’s making that answer reliable, auditable, and repeatable inside business workflows. A practical frame I use: 3 layers, 6 agreements. 1) Context Layer (data + events) -Data contracts: schemas + SLAs for the few sources that matter (not all of them). -Freshness policy: how “up to date” is “good enough” for each use case (90m for CX, 24h for finance, etc.). 2) Decision Layer (models + orchestration) -Decision boundary: what the model is allowed to decide vs. what still needs rules or human review. -Feedback loop: how signals (accept/reject/override) flow back to retraining, on a schedule you can sustain. 3) Control Layer (governance + risk) -Traceability: log prompts, inputs, outputs, and feature versions so you can explain a decision six months later. -Guardrails: PII handling, role-based access, denial lists implemented in code, not policy docs. If those six agreements are explicit, most “AI failures” become integration tickets, not existential debates. What to measure (so this doesn’t drift into theatre): - Cycle time to decision (pre vs. post). - The % of decisions auto-approved within guardrails. - Override rate and top override reasons (drives next iteration). - Time to root-cause a bad outcome (should fall as traceability improves). This isn’t about clever prompts or bigger budgets. It’s boring excellence: clear contracts, bounded decisions, observable systems. Do that consistently, and AI stops being a demo and starts compounding value, quietly. #AI #EnterpriseArchitecture #Governance #SolutioningLeadership #IntelligentTransformation