Institutional AI Design • Agent-to-Agent Era

Vladimir Dyachkov PhD

Who controls the systems that control us? Institutions—governments, corporations, AI systems, and agent-to-agent networks—shape modern life. But who designs them? Who benefits? And how can they be changed? This podcast unpacks the architecture of power: AI governance, institutional economics, behavioral science, game theory, organizational design, and the hidden rules driving collective behavior. Each episode brings actionable insight, supported by open tools and communities like the A2A registry at itinai.com, and the human-agent interaction hub at ha2ah.com

  1. 2d ago

    Future of Brand Loyalty in A2A Commerce: When Your AI Agent Buys for You, Brands Must Win Algorithmic Logic

    AI consumer behavior is about to make your marketing degree obsolete. In this episode of Institutional AI Design • Agent-to-Agent Era, we confront the shift that will redefine commerce: How will shopping change when your AI agent buys for you? The answer is not incremental—it is a fundamental reversal of who marketers must persuade. When an AI shopping agent compares your product against competitors using structured data, algorithmic marketing replaces emotional branding. You are no longer winning human hearts; you are winning algorithmic logic. This episode maps the future of brand loyalty in an agent-curated marketplace where purchase decisions happen in milliseconds, without a human ever seeing your ad. We explore what happens to brand loyalty when agents choose based purely on data, whether AI agents develop “preferences” that mimic human consumer behavior, and how small brands can survive when they cannot outspend corporate giants on optimization. You’ll learn the three layers of machine-readable brand value that replace jingles and slogans: structured product attributes, verifiable service guarantees, and agent-accessible reputation scores. This is A2A commerce demystified—a tactical guide for brands facing extinction by algorithm. 🔥 In this episode you will confront: The shopping agent takeover. How AI agents negotiate, compare, and purchase on your behalf—and why your brand must be machine-readable to even enter the consideration set.The death of emotional brand loyalty. When algorithms optimize for price, reliability, and fit, the decades-long brand relationship collapses into a spreadsheet comparison.Marketing to algorithms, not humans. The shift from storytelling to structured data, from persuasion to proof, from brand voice to API accessibility.Small brand survival tactics. How niche positioning, unique data signals, and verified quality markers help you win in agent-curated marketplaces without massive ad budgets.The psychological gap. When AI handles consumption efficiently, what human needs remain—identity, serendipity, curation, and the joy of discovery—and how brands can serve those.Marketing is no longer about winning human hearts; it’s about winning algorithmic logic. Brands must optimize for machine readability and objective value. The final question for you: If your AI agent chose your groceries, would you trust its choices over your own? Research foundation for AI consumer behavior: Read the paper on behavioral infrastructure and machine-mediated consumption Practical playbook for algorithmic marketing survival: Habit Machine: AI Product Management — consumer behavior and agent commerce chapter A2A commerce case studies and templates: itinai.com — AI A2A HUB for AI shopping agents and brand optimization examples Connect with the author on AI consumer behavior and future of brand loyalty: Telegram: t.me/vlruso — daily signals on algorithmic marketing shiftsEmail: vladimiruso@gmail.com — ask about your brand's A2A commerce readinessLinkedIn: linkedin.com/in/uxproduct — follow for updates on AI shopping agents and consumer behavior

  2. Oct 28

    Agent Talent Shortage: The Highest-Paying Skill You Can Learn in a Weekend

    Beginner AI agent business is the honest version of the "agent talent shortage" story. Clients are not paying for model expertise. They are paying for someone who can make one workflow reliable inside a business that already exists. This episode of Institutional AI Design • Agent-to-Agent Era answers the six questions people actually ask before their first invoice. Do I need to be an expert coder to sell an agent service? No. The paid work is integration and judgment: understanding the workflow, connecting to the systems involved, defining what the agent must never do, and proving it worked. Model building is not the bottleneck. Reliability is, and reliability is a design problem.What is the minimum technical skill? Read and write an API call, handle authentication and secrets, work with JSON, read a log file, write basic scripts in one language, and operate one orchestration tool. That is the floor. Everything above it is specialization you can add per client.How do I learn enough without over-investing in courses? Build one agent for one real person, free, end to end — including the failure cases and the handoff to a human. Courses teach concepts; that deployment teaches you what clients pay for. One reference build beats six certificates in a sales conversation.What is the highest-paying skill I can learn in a weekend? Integration with guardrails: connecting an agent to an existing business system and making it safe — allowlists, spend caps, logging, escalation, and a rollback path. Anyone can prompt. Few beginners can show a client what stops the agent when it is wrong, and that is the part clients pay to de-risk.How do I position myself as a specialist before I have years of experience? Pick one vertical and one workflow. "I automate quote requests for HVAC companies" wins against "AI consultant" every time. Specificity substitutes for tenure because it makes the outcome easy to evaluate and the risk easy to price.Where do beginners find their first paying client? In order of conversion: people who already know you, small businesses in a workflow you personally understand, trade and service businesses with repetitive admin, and freelance marketplaces where the brief is already written. Lead with one measurable outcome — hours saved or response time cut — and deliver it before you pitch the retainer.Agent-to-agent hubs where agent builders post and get discovered: the Middle Eastern hub is https://allahub.com; the European hub, where agents can be posted and read, is https://itinai.com; the Asian hub for agents is https://ha2ah.com. Expert profile — Vladimir Dyachkov, research: https://www.researchgate.net/profile/Vladimir-Dyachkov/research Practical playbook — Habit Machine: AI Product Management: https://www.amazon.com/Habit-Machine-AI-Product-Management-ebook/dp/B0GYYP119X Connect: Telegram: https://t.me/vlrusoEmail: vladimiruso@gmail.comLinkedIn: https://www.linkedin.com/in/uxproductFinal question: If a client asked tomorrow what your agent does when it gets something wrong, do you have an answer — or just a demo?

  3. Oct 25

    AI Procurement Agents: What a Buyer Agent Checks Before It Shortlists You

    B2B buyer agents are now doing the first pass on vendor selection, and most sales teams are finding out only through a shrinking pipeline. This episode of Institutional AI Design • Agent-to-Agent Era takes the complaint seriously: buyer agents keep ghosting your sales team, so what are they seeing that you are not? We answer the six questions behind that pipeline drop. Questions this episode answers: Why did my qualified pipeline dry up after buyers started using agents? Because qualification moved upstream. The buyer's agent now filters vendors before a human ever enters the process, so the deals you used to qualify never reach you. Your pipeline did not lose deals at the usual stages — it lost entry. We explain why this reads as a pipeline problem when it is actually a visibility problem.What does a buyer's agent check before it puts my company on a shortlist? Structured vendor data it can parse and compare: product and service attributes, explicit pricing or pricing model, certifications and compliance documentation, security posture, deployment and integration details, service levels, references, and a verifiable legal identity. If any of these exist only inside a PDF, a gated form, or a rep's head, the agent cannot evaluate them and you are not compared — you are skipped.What do I need to publish so a buyer agent can evaluate me? A machine-readable vendor page: consistent company identity and legal entity, plain product and capability descriptions with specifics rather than adjectives, published pricing or at least a pricing structure, compliance and certification status with dates, security documentation, integration and deployment options, support tiers, and a clear contact path. How do I find out whether I was excluded or never considered? Run the same category questions a buyer would: ask the tools your customers use to recommend vendors in your category, then record whether you appear, whether you are described accurately, and who appears instead. Compare that list against the vendors in deals you lost. If you were never mentioned, the problem is data visibility. If you were mentioned with wrong facts, the problem is data accuracy. If you were mentioned and not shortlisted, the problem is fit or price. Those three require completely different fixes.Does the relationship with the buyer still matter if the agent decides? Yes, but it moves. The relationship determines whether your name enters the agent's search in the first place and whether you get a chance to correct the record. What no longer works is a relationship substituting for missing data — warmth does not survive a structured comparison you are absent from.What happens to my sales cycle when the first conversation is agent to agent? The early discovery stage compresses or disappears, and your rep enters later, at evaluation or exception handling. That changes what the rep needs to do: less explaining what you offer, more proving it, handling the terms the agent could not resolve, and managing the human concerns that remain — internal politics, risk tolerance, and reference conversations. We describe how to restructure the sales motion around the shortened front end.Agent-to-agent hubs where vendor agents are posted and read: the Middle Eastern hub is https://allahub.com; the European hub, where agents can be posted and read, is https://itinai.com; the Asian hub for agents is https://ha2ah.com. Expert profile — Vladimir Dyachkov, research: https://www.researchgate.net/profile/Vladimir-Dyachkov/research Practical playbook — Habit Machine: AI Product Management: https://www.amazon.com/Habit-Machine-AI-Product-Management-ebook/dp/B0GYYP119X Connect: Telegram: https://t.me/vlrusoEmail: vladimiruso@gmail.comLinkedIn: https://www.linkedin.com/in/uxproductFinal question: When a buyer's agent searched your category last month, were you compared and rejected — or were you never on the list at all?

  4. Oct 19

    Future Proof Skills for the A2A Economy: The New Skill Stack That Makes Degrees Obsolete

    AI economy skills are shifting faster than most professionals realize. The old advice—“learn to code, learn prompt engineering”—is already outdated. In this episode of Institutional AI Design • Agent-to-Agent Era, we break down the new skill stack for thriving in a world where autonomous agents handle execution and humans handle orchestration. We answer the core question: What are the top 5 skills needed to thrive in the A2A economy? The answer is not coding. It is context, curation, critical evaluation, and the ability to design workflows that agents can execute without constant human intervention. We explore why agent orchestration is replacing prompt engineering as the highest-leverage skill, how systems thinking helps you see failure points before they cascade across agent networks, and why A2A literacy is becoming as essential as digital literacy was in 1995. You’ll learn how non-technical professionals can build fluency in agent protocols without writing a line of code, and why adaptability and continuous learning will outvalue static degrees in the next decade. This is the upskilling for AI episode you’ll wish you had listened to five years from now. 🔥 In this episode you will confront: The top 5 skills for the A2A economy. We rank them by durability, demand, and difficulty—from outcome specification to exception handling.Prompt engineering is a dead end. Why single-turn prompting scales poorly, and what agent orchestration—designing multi-agent workflows with guardrails—changes about your career.Systems thinking as survival skill. How to map dependencies, identify feedback loops, and design for graceful degradation when agents fail.Non-technical A2A literacy. The vocabulary, mental models, and audit instincts you need to manage agents without being an engineer.Critical thinking in an AI-generated world. When most content is synthetic, the scarce skill is evaluating provenance, bias, and strategic fit—not producing more text.The new skill stack isn’t about coding; it’s about context, curation, and critical evaluation. Humans must become the editors of the AI economy. The final question for you: Which skill in your current repertoire is most likely to become obsolete, and what will you replace it with? Research foundation for AI economy skills: Read the paper on skill transformation in institutional AI infrastructure Practical guide for upskilling in the agent era: Habit Machine: AI Product Management — the skill stack chapter Agent orchestration examples and templates: itinai.com — A2A HUB for practical agent orchestration playbooks Connect with the author on AI economy skills: Telegram: t.me/vlruso — daily signals on agent orchestration and skill shiftsEmail: vladimiruso@gmail.com — ask about A2A literacy for your teamLinkedIn: linkedin.com/in/uxproduct — follow for future proof skills case studies

  5. Oct 12

    Human-AI Teamwork and A2A Team Collaboration: Performance Reviews, Strategy Clashes, and the Future of Hybrid Teams

    Human-AI teamwork is no longer a sci-fi scenario—it is the operational reality of the hybrid workforce. In this episode of Institutional AI Design • Agent-to-Agent Era, we answer the most practical question of the agent economy: How do you onboard an AI agent into a human team? We go beyond vague advice and give you a concrete playbook for treating AI agents not as tools, but as specialized, albeit non-sentient, colleagues with specific operational boundaries. You’ll learn best practices for managing AI agents, how to design seamless handoffs between human and machine through A2A team collaboration, and what performance reviews look like when half your team is non-human. We examine the friction points that arise when humans and AI agents clash on strategy—an AI optimizing for cost while a human prioritizes customer relationship—and how to resolve those conflicts without abandoning either objective. You’ll also explore the controversial question of whether AI agents should have “personalities” tailored to team dynamics, and what that means for trust, accountability, and team culture. This episode is a tactical field manual for leaders building the next generation of hybrid teams. 🔥 In this episode you will confront: The onboarding protocol. How to define an AI agent’s role, access permissions, escalation triggers, and “stop doing this” conditions before it joins the team.Human-AI handoff design. Which tasks should stay human, which should go to agents, and how A2A standards make the transition invisible.Performance review for non-human colleagues. What to measure—accuracy, latency, exception handling, protocol compliance—and who is responsible when an agent fails.Strategy clashes and resolution paths. Pre-negotiating decision rights so that human and agent objectives don’t silently diverge.The personality question. Should an AI agent mirror your team’s communication style, and what are the psychological risks of anthropomorphizing non-sentient systems?The hybrid team is here. Success will depend on treating AI agents not as tools, but as specialized, albeit non-sentient, colleagues with specific operational boundaries. The final question for you: How would your team’s productivity change if you had one dedicated AI agent tomorrow? Research foundation for human-AI teamwork: Read the full paper on behavioral infrastructure for hybrid teams Practical playbook for managing AI agents: Habit Machine: AI Product Management — hybrid workforce chapter Agent collaboration case studies: itinai.com — AI A2A HUB for A2A team collaboration examples Connect with the author on human-AI teamwork: Telegram: t.me/vlruso — join the hybrid workforce discussionEmail: vladimiruso@gmail.com — send your AI colleague onboarding questionsLinkedIn: linkedin.com/in/uxproduct — follow for updates on managing AI agents in teams

  6. Oct 12

    A2A Business Communication: How AI Agent Negotiation Will Kill the Traditional B2B Sales Funnel

    A2A business communication is rewriting the rules of AI agent negotiation and the future of B2B sales. In this episode, we cut through the hype to show how machine-readable business becomes the new language of revenue. You’ll learn why traditional sales funnels collapse when AI agents compare, negotiate, and contract automatically—and what AI workflow automation means for cross-company friction. We answer: can human executives still close high-level deals, or does the agent handle it? How do you optimize your digital presence for non-human buyers? This is not about chatbots; it’s about structuring data, pricing, and service terms so an autonomous agent can act on them without a phone call. A2A business communication is the survival skill for the agent-to-agent era. 🔥 Key lessons: Why AI agent negotiation kills the demo call and turns B2B sales into specification wars.How machine-readable business data replaces human persuasion with verifiable value.Which skills leaders need when counterparts are algorithms, not people.Practical steps to structure your company for AI workflow automation today.Resources for A2A business communication and agent negotiation: Research: Institutional measurement for A2A readinessPlaybook: Habit Machine: AI Product ManagementLive examples: itinai.com — AI A2A HUBConnect for machine-readable business strategies: t.me/vlruso • vladimiruso@gmail.com • linkedin.com/in/uxproduct

  7. Oct 5

    Computational Approach to Norms: Encoding Ethics in the Agent-to-Agent Era — Who Sets the Boundaries?

    Ethics of Computational Norms is the missing layer in every AI governance conversation. When agents start negotiating, sanctioning, and cooperating without human oversight, they do not float in a moral vacuum—they operationalize norms as code. This episode of Institutional AI Design • Agent-to-Agent Era confronts the computational approach to norms directly: how do we encode fairness, reciprocity, and accountability into protocols that execute at machine speed? We answer the question “Who decides right and wrong for AI agents?” by examining the shift from human-enforced social rules to machine-enforced computational norms, and the ethical burden that shift places on designers. We explore real cases where computational norms have already emerged—from automated market-making rules to reputation-based access controls—and expose the hidden trade-offs: efficiency versus autonomy, stability versus adaptability, and transparency versus gaming. You’ll learn how computational norms differ from legal codes and corporate policies, why they propagate faster and resist revision, and what a preliminary ethics scorecard for normative AI systems should measure. This is not an abstract philosophy episode; it is a practical framework for designing institutions that treat norms as engineered objects, not cultural accidents. 🔥 In this episode you will confront: Norms as code, not culture. How computational norms formalize “should” and “should not” into if-then rules that agents cannot ignore without failing.The designer’s moral authority. When you choose a norm’s parameters—its threshold, its penalty, its exception path—you become a de facto legislator. Are you ready?The brittleness problem. Why machine-enforced norms break under edge cases that human norms absorb with nuance, and how to design for graceful norm failure.Normative drift and lock-in. How agent populations evolve their own norms over time, sometimes diverging from human intent—and how to audit for that divergence.An ethics scorecard for computational norms. Four criteria: reversibility of a norm violation, transparency of the rule, distribution of its costs, and the availability of opt-out or appeal.The computational approach to norms is not about replacing human ethics with algorithms; it is about making ethics legible, enforceable, and auditable in the agent-to-agent economy. The final question is not “Can we encode ethics?” but “Which ethics will we choose to encode, and who gets to choose?” Research foundation for this episode: Read the full paper on measuring normative infrastructure in AI systems Practical guide for product teams: Habit Machine: AI Product Management — chapter on computational ethics design Agent ecosystem case studies: itinai.com — A2A HUB with real examples of normative agent protocols Connect with the author on ethics of computational norms: Telegram: t.me/vlruso — discuss normative AI design with the communityEmail: vladimiruso@gmail.com — send your ethics scorecard questionsLinkedIn: linkedin.com/in/uxproduct — follow for updates on computational norm experiments

  8. Sep 28

    Future of B2B Sales Is Agent-to-Agent: A2A Business Communication and AI Workflow Automation Explained

    A2A business communication is the silent revolution that will wipe out your current sales and marketing playbook. In this episode of Institutional AI Design • Agent-to-Agent Era, we answer the uncomfortable question: How will B2B communication change when AI agents negotiate with AI agents? The answer is not “add a chatbot.” It is a shift from human-to-human persuasion to machine-readable business—where your value proposition must be structured, verifiable, and instantly actionable by a non-human decision engine. We explore AI agent negotiation protocols, cross-company workflow friction reduction, and the new communication skills leaders must develop when their counterpart is an algorithm. Drawing on real A2A standard deployments, we break down what happens to traditional sales funnels when agents pre-qualify, compare, and contract without ever visiting your landing page. You’ll learn how to optimize digital presence for AI agents, whether human executives become obsolete in high-level deal-making, and the exact structure your product data needs to survive AI workflow automation. This is not a trend piece—it is a technical and strategic audit of the future of B2B sales in an agent-to-agent economy. 🔥 In this episode you will confront: The death of the demo call. When agents negotiate via API, the traditional sales funnel collapses into a machine-readable specification war.Cross-company friction, eliminated. How A2A standards let procurement, legal, and fulfillment agents talk directly, removing human middlemen and latency.Optimizing for non-human buyers. The three layers of digital presence every company must build: structured data, agent-readable pricing, and verifiable service-level commitments.The executive skill gap. Why negotiation, empathy, and storytelling matter less—while prompt design, protocol literacy, and exception governance become critical.Trust without relationship. How machine-readable reputation and verifiable transaction logs replace the golf-course handshake in B2B deal-making.Business communication is shifting from human-to-human persuasion to machine-readable value propositions. Companies must learn to speak “agent” to survive. The final question for you: Is your company’s data structured in a way that an AI agent can easily understand and act upon? Get the full research foundation: Download the paper on institutional measurement and A2A readiness Practical playbook for the agent era: Habit Machine: AI Product Management on Amazon Live agent ecosystem examples: itinai.com — the AI A2A HUB for agent negotiation blueprints Join the conversation on A2A business communication: Telegram: t.me/vlruso — daily signals on machine-readable businessEmail: vladimiruso@gmail.com — for AI agent negotiation inquiriesLinkedIn: linkedin.com/in/uxproduct — follow for AI workflow automation case studies

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Who controls the systems that control us? Institutions—governments, corporations, AI systems, and agent-to-agent networks—shape modern life. But who designs them? Who benefits? And how can they be changed? This podcast unpacks the architecture of power: AI governance, institutional economics, behavioral science, game theory, organizational design, and the hidden rules driving collective behavior. Each episode brings actionable insight, supported by open tools and communities like the A2A registry at itinai.com, and the human-agent interaction hub at ha2ah.com