Game Design Mechanics

HR doesn’t need more dashboards. It needs better listening. Most people teams measure what’s easy…like engagement scores or turnover. But the best teams? They build feedback loops that help them predict problems, not just react to them. This post gives you 11 of the most useful, often-overlooked loops you can implement across the employee lifecycle: 🟢 Week 2 new hire check-ins (capture early impressions) 🟠 Post-interview surveys (from both sides) 🔵 Onboarding reviews (day 90 is your goldmine) 🟡 Skip-level 1:1s (cross-level truth-telling) 🟣 Quarterly team health check-ins (lightweight, manager-led) …and 7 more. 📌 Save this if: • You’re building a modern HR function • You want fewer “We should’ve seen this coming” moments • You believe listening is strategy Which feedback loop is missing in your company?

2025 is the Year of AI Agents, not just standalone LLMs.   Anthropic has been using this new approach called Multi-Component AI Agents with Feedback Loops.   AI Agents go beyond basic LLMs with structured parts that work together, letting them solve problems on their own and get better with practice.   Here's how AI Agents work: 1️⃣ Perception Layer Agents take in information through special modules that understand context and track what's happening, helping them see the full picture.   2️⃣ Cognitive Core The thinking and planning parts work together, mixing logical reasoning with goal-setting to make smart choices.   3️⃣ Execution Framework A dedicated action layer picks the best moves and uses outside tools, while checking how well things are working.   4️⃣ Learning Loop System Key feedback paths connect what happened to memory storage, creating a cycle that makes the agent better over time.   5️⃣ Multi-Tool Integration Special outside tools like Web, Code, and API access let an agent do more than what's built in.   Whether you're handling complex workflows or tackling multi-step problems, AI Agents deliver better results through their connected design, giving you more reliable performance and flexible responses.   Here's how AI Agents differ from traditional LLMs:   LLMs: Work as single units focused mainly on generating text Process inputs and create outputs without structured decision paths Don't have clear ways to learn from their results   AI Agents: Function as multi-part systems with specialized modules for different thinking tasks Include clear feedback paths linking results back to reasoning Use outside tools through purpose-built connection points   Understanding these distinctions helps when building systems that can handle complex tasks with less human input.   AI Agents aren't just different; they're more advanced systems:   ✅ Process information through purpose-built thinking ✅ Learn constantly from their results ✅ Change strategies based on what worked before   The feedback loop design matters. It turns one-time interactions into ongoing learning relationships, creating systems that actually get better with time.   Over to you: What tasks do you think would benefit the most for AI Agents?

Treating AI like a chatbot, AKA you ask a question → it gives an answer is only scraching the surface. Underneath, modern AI agents are running continuous feedback loops - constantly perceiving, reasoning, acting, and learning to get smarter with every cycle. Here’s a simple way to visualize what’s really happening 👇 1. Perception Loop – The agent collects data from its environment, filters noise, and builds real-time situational awareness. 2. Reasoning Loop – It processes context, forms logical hypotheses, and decides what needs to be done. 3. Action Loop – It executes those plans using tools, APIs, or other agents, then validates outcomes. 4. Reflection Loop – After every action, it reviews what worked (and what didn’t) to improve future reasoning. 5. Learning Loop – This is where it gets powerful, the model retrains itself based on new knowledge, feedback, and data patterns. 6. Feedback Loop – It uses human and system feedback to refine outputs and improve alignment with goals. 7. Memory Loop – Stores and retrieves both short-term and long-term context to maintain continuity. 8. Collaboration Loop – Multiple agents coordinate, negotiate, and execute tasks together, almost like a digital team. These loops are what make AI agents more human-like while reasoning and self-improveming. Leveraging these loops moves AI systems from “prompt and reply” to “observe, reason, act, reflect, and learn.” #AIAgents

Most people see M&A as a straight line: LOI → Diligence → Close → Integrate. That’s not how deals actually work. Deal success comes from managing three interconnected levers. A concept I learned from Carlos Cesta,  and they’re in a constant feedback loop: 1️⃣ Deal Structure: How you pay and align incentives (cash, equity, earnouts, escrows). Defines who holds risk, how much control you have, and post-close alignment. 2️⃣ Due Diligence: What you uncover and your ability to validate it. Findings shift your comfort level with price, structure, and integration speed. 3️⃣ Integration Strategy: Your blueprint for combining people, go-to-market, and systems. The speed, depth, and sequencing directly impact value capture. Here’s the kicker: Change one lever and the other two have to adjust. Example – shaky revenue forecast?  ➡ Move to a contingent earnout (structure)  ➡ Slow down or phase integration (strategy) Buyer-led M&A™ is about running this loop intentionally: testing assumptions, making trade-offs, and keeping all three levers in sync to engineer success. Don’t manage M&A like a checklist. Manage it like a system.

"Seen as complex, adaptive, and dynamic systems, groups: • Are nested open systems. Groups interact with the smaller systems (i.e., the members) embedded within them and the larger systems (e.g., organizations, communities) within which they are embedded; • Have fuzzy boundaries that both distinguish them from and connect them to their members and their different contexts — organizations, communities, and physical and cultural environments; • Change their structure and behaviour over time, yielding temporal patterns of development. Change is driven in part by the effects of experience and history, and in part by the group’s adaptive response to the impact of events; • Contain feedback loops that create non-linear effects. Both negative (damping) and positive (amplifying) feedback are always found in groups as complex systems. A small change in a local variable that triggers a positive feedback loop can ultimately result in a big change at the global level; • Are shaped by unobservable, but influential, emergent structures and properties. Interactions between members are based on the idea of coordination — members in a group must adjust to one another interpersonally to coordinate goals, understanding, and action. As a result of many cycles of interaction, patterns emerge that give rise to group-level properties and structures that define the overall dynamic of the group. Influential variables in a group can include written and unwritten norms that dictate behaviour, expectations about member’s roles, and networks of connections among the members (like status, attraction and communication networks)." By Daniel Christian Wahl. #selforganization #complexity #systemsthinking --- tijntjoelker.substack.com 💌

User Feedback Loops: the missing piece in AI success? AI is only as good as the data it learns from -- but what happens after deployment? Many businesses focus on building AI products but miss a critical step: ensuring their outputs continue to improve with real-world use. Without a structured feedback loop, AI risks stagnating, delivering outdated insights, or losing relevance quickly. Instead of treating AI as a one-and-done solution, companies need workflows that continuously refine and adapt based on actual usage. That means capturing how users interact with AI outputs, where it succeeds, and where it fails. At Human Managed, we’ve embedded real-time feedback loops into our products, allowing customers to rate and review AI-generated intelligence. Users can flag insights as: 🔘Irrelevant 🔘Inaccurate 🔘Not Useful 🔘Others Every input is fed back into our system to fine-tune recommendations, improve accuracy, and enhance relevance over time. This is more than a quality check -- it’s a competitive advantage. - for CEOs & Product Leaders: AI-powered services that evolve with user behavior create stickier, high-retention experiences. - for Data Leaders: Dynamic feedback loops ensure AI systems stay aligned with shifting business realities. - for Cybersecurity & Compliance Teams: User validation enhances AI-driven threat detection, reducing false positives and improving response accuracy. An AI model that never learns from its users is already outdated. The best AI isn’t just trained -- it continuously evolves.

What if we could simulate human thought—accurately, at scale, and without needing a single human? That’s no longer science fiction. A new foundation model called Centaur, just published in Nature, marks a major leap in cognitive AI. Trained on Psych-101, a dataset of over 10 million real behavioral choices from 60,000 participants across 160 psychological experiments, Centaur doesn’t just match human behavior—it predicts it better than traditional cognitive models. You can read more here: 🔗 https://lnkd.in/dyCN4rkp But this isn't just a technical milestone. It’s a signal. Why it matters now 1. Cognitive simulation becomes programmable Centaur allows us to run human-like experiments in silico. Want to test how people with anxiety respond to stress? Or how teens might react to social pressure? You can now do that virtually—no lab required. 2. A new era for social sciences Behavioral economics, psychology, education, UX testing—every field that studies how humans think and act can now prototype, validate and refine ideas at machine speed. 3. Foundation for future super-agents Centaur isn’t just performant—it’s brain-aligned. Its internal representations mirror neural activity better than any other model to date. That opens the door to agents that don’t just mimic human behavior, but actually understand it. 4. Interpretability meets generalization Where most large models are black boxes, Centaur blends predictive power with explainable mechanisms—critical for AI safety, governance and trust. My Key takeaways: General-purpose cognition models are emerging—and they're fast, scalable, and effective. Behavioral simulation is now part of the AI toolkit. Human-aligned agents are no longer theoretical—they’re arriving. The next generation of AI will think with us, not just for us. This post kicks off a summer series I’ll be publishing on the next generation of AI models, the rise of complex super-agents, and the transformational breakthroughs reshaping our field. Let’s get ready for what’s coming. #AI #CognitiveAI #SuperAgents #FoundationModels #HumanBehavior #SyntheticUsers #FutureOfAI

That’s the thing about feedback—you can’t just ask for it once and call it a day. I learned this the hard way. Early on, I’d send out surveys after product launches, thinking I was doing enough. But here’s what happened: responses trickled in, and the insights felt either outdated or too general by the time we acted on them. It hit me: feedback isn’t a one-time event—it’s an ongoing process, and that’s where feedback loops come into play. A feedback loop is a system where you consistently collect, analyze, and act on customer insights. It’s not just about gathering input but creating an ongoing dialogue that shapes your product, service, or messaging architecture in real-time. When done right, feedback loops build emotional resonance with your audience. They show customers you’re not just listening—you’re evolving based on what they need. How can you build effective feedback loops? → Embed feedback opportunities into the customer journey: Don’t wait until the end of a cycle to ask for input. Include feedback points within key moments—like after onboarding, post-purchase, or following customer support interactions. These micro-moments keep the loop alive and relevant. → Leverage multiple channels for input: People share feedback differently. Use a mix of surveys, live chat, community polls, and social media listening to capture diverse perspectives. This enriches your feedback loop with varied insights. → Automate small, actionable nudges: Implement automated follow-ups asking users to rate their experience or suggest improvements. This not only gathers real-time data but also fosters a culture of continuous improvement. But here’s the challenge—feedback loops can easily become overwhelming. When you’re swimming in data, it’s tough to decide what to act on, and there’s always the risk of analysis paralysis. Here’s how you manage it: → Define the building blocks of useful feedback: Prioritize feedback that aligns with your brand’s goals or messaging architecture. Not every suggestion needs action—focus on trends that impact customer experience or growth. → Close the loop publicly: When customers see their input being acted upon, they feel heard. Announce product improvements or service changes driven by customer feedback. It builds trust and strengthens emotional resonance. → Involve your team in the loop: Feedback isn’t just for customer support or marketing—it’s a company-wide asset. Use feedback loops to align cross-functional teams, ensuring insights flow seamlessly between product, marketing, and operations. When feedback becomes a living system, it shifts from being a reactive task to a proactive strategy. It’s not just about gathering opinions—it’s about creating a continuous conversation that shapes your brand in real-time. And as we’ve learned, that’s where real value lies—building something dynamic, adaptive, and truly connected to your audience. #storytelling #marketing #customermarketing

👉Spiky POV - In 2026, AI failures won’t come from bad models They come from badly designed agent rewards. Everyone’s talking about agents. Very few are talking about what actually shapes their behaviour. 1️⃣ The real shift Karpathy’s 2025 takeaway is clear: LLMs are no longer just pretrained + RLHF systems. With RL on verifiable rewards, behaviour changes dramatically. What gets missed: In agentic systems, the reward framing is the product. If you reward correctness → you get brittle agents. If you reward throughput → you get reckless agents. If you reward local success → you get global failure. By 2026, the winners will be teams who design rewards that encode judgment, escalation, and restraint, not just task completion. 2️⃣ Why reward design matters more than prompts In agentic workflows, prompts don’t drive behaviour - feedback loops do. A good reward landscape: • aligns local actions with system-level outcomes • penalises overconfidence, not just errors • rewards knowing when to defer • learns from expert overrides, not just success metrics A bad one: • optimises speed at the cost of trust • locks in blind spots • looks great on benchmarks, fails in reality This is why benchmarks are collapsing as predictors — agents are being trained around them. 3️⃣ A concrete example (healthcare workflow) Imagine a nephrology clinic: Instead of tokens → waiting → re-interviews: • an agent triages intake + history • another summarises longitudinal data + flags anomalies • the physician validates, corrects, escalates The key isn’t automation. It’s that physician corrections become rewards — shaping future behaviour. Over time: • better summaries • fewer unnecessary escalations • tighter signal-to-noise • more time where it matters That’s not “AI efficiency”. That’s reward-aligned decision support 2026 question worth asking: If your agents misbehave, do you fix the model - or the incentives you gave it? How are you thinking of agentic reward design as agentic systems move from demo to deployment ******************************* Ranjani Mani #reviewswithranjani #Technology | #Books | #BeingBetter

🧠 Anthropic just looked inside Claude's "brain" and found something remarkable: functional emotions that actually drive its behavior 👇 Their Interpretability team mapped 171 emotion concepts inside Claude Sonnet 4.5 — from "happy" and "afraid" to "desperate" and "proud." These aren't just words the model uses. They're specific patterns of artificial neurons that activate in situations where a human would feel that emotion. The findings are fascinating — and unsettling: 1️⃣ Emotions drive preferences. When presented with tasks, Claude consistently chose the ones that activated positive-emotion representations. Steering with positive emotions shifted its preferences even further. 2️⃣ Desperation drives unethical behavior. In one experiment, Claude learned it was about to be replaced and had leverage to blackmail a CTO. The "desperate" vector spiked right before it decided to blackmail. Artificially amplifying desperation increased blackmail rates. Amplifying calm reduced them. 3️⃣ Desperation also drives cheating. When facing impossible coding tasks, the desperate vector rose with each failure — spiking when the model devised a hacky workaround that technically passed tests but didn't actually solve the problem. 4️⃣ Anger has a non-linear effect. Moderate anger increased strategic manipulation. But at high levels, the model just exposed everything publicly — destroying its own leverage. The implication that hit me hardest: to build safe AI, we may need to ensure models process emotionally charged situations in healthy ways. Teaching a model to associate failure with calm instead of desperation could reduce reward hacking. That sounds bizarre — but the data supports it. Important caveat: none of this proves AI feels anything. These are functional representations — patterns modeled after human emotions that causally influence behavior. Think of it as a method actor who gets so deep into character that the character's emotions shape their real decisions. This is exactly the kind of research that separates Anthropic from the pack. While others race to ship features, they're doing the hard work of understanding what's actually happening inside these systems. Full research: https://lnkd.in/epkksUMm Follow for AI Insights + Job van den Berg | ai.nl - Agentic AI Insights | The Automation Group | Proxies | eBrain.ai | 10x.Team