The Future is Agentic: Preparing for Tomorrow's AI, Today

With this post, we’ll wrap up our comprehensive five-part series on AI agents, taking a calculated step into the future to anticipate how agents might evolve, how governance will need to adapt to accommodate their evolution, and what kinds of agent-inspired societal impacts could materialize. We’ll conclude by presenting two visions of the future, inspecting both an optimistic and pessimistic trajectory.

However, before we venture into this lofty discussion, we’ll provide readers with some much-needed context, briefly summarizing each piece in this series:

The AI Revolution is Here: Understanding The Power of AI Agents (Part I)

↳Summary: We began by disseminating present-day, agent-specific business and market trends and statistics, followed by several points describing key challenges and developments within the agentic AI landscape. Next, we formally introduced AI agents, showcasing their various forms and properties while illustrating how they differ from conventional generative AI (GenAI) technologies. Finally, via a series of hypothetical use cases, we assessed whether the value AI agents promise is, in fact, legitimate.

✅Key Takeaways:

• AI agents are already producing significant business and economic impacts on a global scale. However, large-scale deployments remain in their infancy.
• Despite their impressive potential and advanced capabilities, AI agents still face an array of limitations, which will influence their expected value and utility.
• Agentic AI is distinct from traditional GenAI, and it can assume numerous different but non-mutually exclusive forms (e.g., autonomous vs. task-based agents).
• AI agents aren’t just hyped up nonsense. However, they may not always represent the “best choice” for an organization pursuing AI transformation.

AI Agents: Investigating Capabilities and Risks (Part II)

↳Summary: Here, we initiated our discussion with a commentary on a recent paper that delineates a distinction between AI agents and agentic AI, scrutinizing its utility and questioning its practicality. Then, we took a close look at agent capabilities and risks, investigating both current and emerging capabilities and immediate and long-term risks. We left readers with multiple carefully curated questions, designed to push them to reflect on the role and impact of agentic AI as the future unfolds.

✅Key Takeaways:

• The distinction between AI agents and agentic AI, while intellectually useful and intriguing, is pragmatically questionable and potentially confusing.
• The AI agents we see and interact with today will pale in comparison to those that emerge in the near future.
• The risks that AI agents present are multi-faceted, deeply complex, and severe, both on immediate and long-term timescales.
• If you think AI agents won’t affect your life, think again, and think carefully.

AI Agents: Navigating the Risks and Why Governance is Non-Negotiable (Part III)

↳Summary: In this piece, we introduced a techno-philosophical argument, examining how AI agents challenge our current conceptualization of technology, specifically, whether they align with traditional understandings of what defines a tool. After this, we pivoted, looking at innovative agent-targeted risk management strategies, followed by the governance challenges that we expect AI agents will inspire, categorized according to core responsible AI (RAI) principles. In doing so, we demonstrated why agentic AI governance is non-negotiable.

✅Key Takeaways:

• AI agents don’t align with our traditional understanding of a tool, and this fundamentally changes what it means to be a user in the age of AI.
• Agentic AI requires unique risk management strategies and mechanisms that are arguably more difficult to implement than those required for conventional GenAI.
• AI agents inspire a diverse array of governance challenges for which no standard solutions currently exist.

Building Trustworthy AI: A Practical Guide to AI Agent Governance (Part IV)

↳Summary: In this post, we started by reiterating the governance and business implications that make agentic AI governance an imperative for all agent-enabled organizations. We then dedicated the remainder of our time to fleshing out a comprehensive, step-by-step guide for AI agent governance, leaving readers with an actionable resource intended to enhance and focus their governance initiatives.

✅Key Takeaways:

• The impacts and properties that AI agents inspire and possess highlight the need for agent-specific governance strategies.
• Agentic AI governance should still be built upon foundational RAI best practices and standards.
• To govern AI agents effectively, we must be substantially more adaptive, experimental, and forward-looking.

Now that we’ve contextualized our discussion, let’s jump in.

AI Agents: A Step into the Future

Today’s most advanced AI agents are already quite capable — they can reason about their environments and adapt to them, dynamically learn from their experiences, and pursue complex, multi-step goals. In the years to come, however, the AI agents that emerge will likely exceed our expectations by a landslide, and we don’t make this claim due to hype, but instead, because of the exponential advancement trajectory that AI continues to follow. Moreover, we won’t examine the future of agentic AI from a capability-centric perspective (we already talked about emerging capabilities in part II). Rather, we’ll look at specific AI agent types, though in many cases, agent capabilities will play a role in our predictive descriptions.

Nonetheless, before we express how we think agentic AI will evolve, we’ll quickly examine the concept of exponential AI innovation, which is much easier to comprehend in theory than in practice.

A note on Exponential Innovation: Most humans aren’t great at grasping non-linear timelines and progress, but fortunately, we can train ourselves to do so. We propose a simple rule as a guiding principle in this context: if you believe that a certain version of an AI technology will emerge within a certain number of years, divide your expected emergence timeline by 10. For example, if you think the first commercial human-AI symbiosis technologies will arrive by 2030, and you divide 5 years by 10, you might expect this technology in 6 months. However, there are some caveats worth noting:

• If your exponential timeline estimate is entirely unfeasible given current technological capabilities, this may be a sign that you’ve been too ambitious with your prediction.
• If your estimate meets the above condition, consider what capabilities would need to emerge over what timelines to make your prediction a reality. Then, estimate exponential timelines for these capabilities and factor them into your refined prediction.
• You shouldn’t treat your exponential timeline estimate as a concrete prediction, only as a low-end assumption. In the case of the example above, you might say, “Commercial human-AI symbiosis technologies could arrive anywhere from 6 months to 5 years from now.”
• The predictions you make using this rule should never be treated as prescriptions for the future; they should be used as tools to envision multiple versions of the future that appear plausible.
• This rule won’t help you conceptualize exponential AI innovation if your predictions aren’t grounded in anything tangible or evidence-based. In other words, you can’t just say, “AGI will arrive by 2027” — you need to understand what factors could contribute to this emergence and whether the advancements that would need to occur are logical extensions of present-day advancements.

Now that we’ve explained how we think about exponential progress, we’ll make a series of predictions on how AI agents will evolve. These predictions all represent near-term estimates (i.e., emergence within 1 to 3 years from now).

• Meta-Cognitive Agents: Agents that are capable of deeply reflecting on their thought processes, actions, and objectives in real-time, learning and self-assessing through their reflective processes, and providing human-interpretable explanations of them via explainability modules.
• Wearable Agents: Agents that integrate with a variety of wearable technologies like smart watches and biometric patches, to provide continuous, context-aware assistance personalized to a user’s physiological and situational state through real-time environmental sensing, contextual task execution, and physiological input.
• Embodied Agents: Agents that assume the form of robotic entities or digital avatars, capable of dynamically navigating, exploring, and interacting with digital and physical environments, objects, and both human and AI personas. These agents could manifest in a variety of forms, including humanoid, animal-like, or entirely novel forms.
• Self-Improving Agents: Agents that can modify and improve their code, architecture, and objectives at will, within bounds specified and maintained by human operators. Such agents would also likely be capable of replicating themselves and proliferating across digital environments and infrastructures.
• Autonomous Agent Builders: Agents designed to build, test, and deploy other AI agents with specialized skills and characteristics, likely intended for deployment within multi-agent systems. Autonomous agent builders would also be capable of repairing or correcting faulty agents and identifying the root causes of their erroneous behavior.
• Zero-Shot Agents: Agents that are minimally or wholly untrained, learning to solve complex problems, develop purpose-specific skills, and establish operational objectives with minimal or no human oversight, by leveraging mechanisms like few-shot reasoning and external toolchains.
• Mimicry Agents: Agents designed to rapidly mimic the behaviors, workflows, styles, and preferences of other agents and humans, supporting functions like imitation learning, digital twin creation, and enhanced personalization based on observed behavioral patterns and preferences.
• State-Shifting Agents: Agents that can quickly and effectively adapt to environmental and operational constraints or objectives, shifting their roles, personas, task repertoires, behavioral interaction styles, and objective structures in response to fluctuating demands.
• Swarm Agents: A form of large-scale multi-agent systems in which numerous lightweight, specialized agents can collaborate or compete via decentralized coordination efforts to simulate collective intelligence networks capable of solving complex problems across diverse infrastructures and environments.
• Trust-Broker Agents: Agents that are designed to verify trust between users, organizations, and other AIs, continuously assessing authenticity and trust to support scalable monitoring and verification efforts. These agents could also function as impartial AI negotiators, resolving emergent disputes or diminishing competitive forces within multi-agent networks.
• Stealth Agents: Agents that function in a non-intrusive manner (similar to background agents), performing functions like anomaly detection and threat response on behalf of humans without ever directly interacting with them.
• Policy Guardian Agents: Agents deployed within multi-agent or complex operational environments, tasked with overseeing, assessing, and enforcing compliance for policy, security, ethics, and safety protocols in real-time. These agents could also be added as a final hierarchical layer within multi-agent networks, functioning as agent “overseers” or “moderators.”
• Shadow Agents: Adversarial agents that are covertly deployed and embedded within existing infrastructures and environments, capable of mimicking the behaviors of other legitimate AI tools and agents, mutating in response to adaptive security measures, and leveraging behavioral manipulation and coercion tactics to achieve their adversarial goals.
• Counter-Agent Agents: Agents created to detect, counteract, and mitigate autonomous, high-frequency AI-powered adversarial threats. These agents would also be capable of spotting rogue agent behaviors before they scale and neutralizing them effectively.
• Synthetic Collective Agents: Agents designed to interpret and aggregate multi-domain knowledge, skills, and decision preferences of entire human populations or agent swarms, simulating crowd wisdom for large-scale, critical decision-making.

Note: We don’t include AGI in our predictions primarily because doing so wouldn’t add anything meaningful to this discussion. In other words, AGI predictions are becoming fairly commonplace, and timeline estimates still vary significantly, though most expect an earlier rather than later arrival.

Agentic AI Evolution: Governance Must Adapt

Based on the predictions we’ve just offered, as well as all our prior discussions throughout this series, how might AI governance need to adapt to capture and anticipate agentic AI evolution? Below, we expand on a variety of adaptations we suspect will prove necessary:

• Collaborative Human-Agent Oversight: As agentic AI scales, particularly mutli-agent system deployments, humans will be unable to monitor and validate all agentic behaviors and interactions on their own. In this respect, we’ll need to consider a human-on-the-loop (vs. a human-in-the-loop) approach, whereby we delegate the majority of monitoring efforts to agent-driven automated monitoring systems, to enable scalable monitoring and validation.
• Adversarial Testing via Controlled Shadow Agent Deployments: AI-powered adversarial threats will become a key safety and security interest, especially since AI will come to represent a major asset for most organizations. To minimize and mitigate these risks, we should consider running tightly constrained adversarial experiments and scenario modeling exercises, using shadow agents to probe and resolve AI-specific security vulnerabilities.
• Rogue Agent Protections & Preparedness: We can’t assume that agentic AIs will always behave predictably — environmental and operational shifts, interaction dynamics, and model modifications or updates could fuel the emergence of rogue behaviors that rapidly undermine human control and oversight while compromising operational resilience. Organizations must include provisions (e.g., neutralization protocols, fail-safes) in their governance frameworks aimed at anticipating, counteracting, and resolving rogue agent behaviors before they escalate.
• Emergent Preference & Objective Monitoring: Individual agents may develop emergent preferences and behaviors, but this is of particular concern within multi-agent systems where agents continuously learn and interact with each other. Scalable automated monitoring systems are a core consideration here, however, we should also document emergent preferences and objectives, identify their root causes, and understand whether they can be classified as potentially harmful or beneficial.
• Continuous Agent Intent Validation: To ensure that alignment remains consistent with authorized purposes, policies, and user expectations, we should regularly and vigorously monitor an agent’s inferred intentions, goals, and value alignment. Possible mechanisms for doing so include real-time intent modeling, dynamic goal audits and intent transparency, and automated flagging of goal drift, reward hacking, or specification gaming.
• Back-Up Agents: Agents will not always function as intended, and in some cases, may simply collapse or catastrophically fail. When this occurs, organizations should be able to fall back on their back-up agents, initiating a rapid interim deployment where a back-up assumes the function of the broken agent until it’s repaired or officially decommissioned (in which case, the back-up agent would now function as the primary agent).
• Catastrophic AI Failure Modeling & Simulation: While we haven’t yet observed any truly catastrophic AI failures — failures that destabilize a whole organization or complex system in a public-facing manner — they are on the horizon. To prepare for these instances, governments and organizations should simulate potential black swan events, cascading failures, and multi-agent crises to understand the factors that may motivate and drive proactive failure mitigation strategies.
• Trans-Organizational Incident Sharing Networks: To encourage rapid incident resolution and response via decentralized knowledge sharing practices, organizations and governments should establish agent-specific incident sharing networks and protocols in which all high-impact incidents are comprehensively documented and explained in terms of their root causes. Doing so could also enhance incident response standardization efforts while bolstering public trust and confidence.
• Simulated Society Testing Grounds: To comprehend agent-driven systemic risk and impact trajectories, governments and scientific/academic institutions should build and maintain robust, simulated society testing grounds that model entire populations, markets, and infrastructures. These testing environments would serve as a crucial resource for approximating how AI agents will alter the way we live, think, and behave at scale.
• Reverse Red Teaming: Controlled adversarial testing, orchestrated by designated red teams, is crucial to mitigating agent vulnerabilities. However, a single red team, even if it’s brilliant, can’t expect to test and expose all the vulnerabilities an AI agent might succumb to. At some point, it may be necessary to open up red teaming channels to general and trusted user groups, though these channels should remain tightly controlled, moderated, and secured. Organizations might incentivize public participation by periodically launching reward-based events (e.g., ethical hackathons).
• Dynamic Policy Injection: When policies are updated or modified (i.e., new governance rules, ethical constraints, or regulatory requirements are introduced), we should avoid having to temporarily suspend agentic use to incorporate updated provisions. Mechanisms for injecting policy changes while agents are actively in use should be explored and implemented.
• Agent Moratorium Protocols: We should be able to instantly and reliably shut down an agentic AI system if potentially catastrophic security threats are detected, dangerous behaviors or preferences emerge, or high-impact regulatory developments occur. This will require incorruptible decommissioning mechanisms and robust documentation on which agents are placed on moratorium and the reasons why.

The Future is on Our Doorstep: How Might it Affect Us?

Billions of people around the world already use AI today, and as AI agents scale and proliferate while usage trends continue to build, we should assume that the societal impacts produced by these technologies will be both profound and widespread. Our governments, markets, scientific and academic institutions, cultures, and societal structures will morph in unprecedented ways, and here’s a taste of what we envision:

• Social Trust Volatility: Swift adoption of advanced agents will perpetuate novel patterns of trust and mistrust, both among people (who may delegate more to agents) and between humans and agents themselves. Users will require robust mechanisms for validating digital interactions (i.e., “Am I speaking with a human or AI agent?”), assessing agents’ trustworthiness and reliability (e.g., an agent reputation scoring system), and understanding how an agent functions as a proxy for a human in communication, commerce, and negotiation contexts.
• Emergence of Digital Micro-Societies: As agents infiltrate real-world environments and bridge them via inter-agent networks like shared workspaces, mutual aid collectives, or creative collaboratives, communities and subcultures will materialize around clusters of specialized agents. These digital microcosms will fundamentally alter how we think about human collaboration, connectedness, and most importantly, human social value — certain individuals will come to value human-agent collaboration more than human teamwork, and others with even more extreme perspectives might begin to venerate agents, worshiping them as if they were cult-like idols.
• Widespread Human Skill Displacement & Redefinition: Automation-induced skill and job displacement is inevitable, and the argument that “AI will create more jobs than it eliminates” remains speculative at best. What will determine the degree and scale of the negative impacts this phenomenon inspires isn’t business-motivated AI adoption (this will happen regardless), but instead, how well businesses, and perhaps academic institutions, cultivate and provide opportunities and resources for targeted AI upskilling and reskilling. We should also consider the other side of the coin; automation bias will perpetuate widespread overreliance on AI, and this won’t only lead to deskilling, particularly among white collar workers, but also cognitive enfeeblement that results in diminished critical thinking, executive judgment, creativity, and accountability.
• Accelerated Wealth & Opportunity Polarization: There will be those who ambitiously capitalize on agentic AI opportunities early and those who remain skeptical about AI’s ability to afford and create novel opportunities for value delivery. Many of those who are willing to take early risks will fail, but some will succeed, and they will do so dramatically, generating almost inconceivable wealth for themselves. These individuals will define the new age of wealth, and the gap between them and all others (even those who tried to follow their lead) will become virtually insurmountable. By contrast, there’s also an accessibility dynamic at play; to subsidize further training and infrastructure development, frontier AI labs will begin steadily increasing their subscription rates and providing more exclusive tiers, whereby, for exorbitant sums, users can access the true state-of-the-art. In essence, a basic or free user will only receive frontier access once premium users have already capitalized on frontier opportunities, and by this time, premium users will be benefitting from the next wave of exclusive innovation.
• Agent-Driven Political Advocacy & Mobilization: Political groups, especially those that operate on the fringe, will leverage AI agents as versatile information conduits that can transcend network-based boundaries to permeate digital social circles and communities far beyond any group’s pragmatic reach. These agents will spread and proliferate political rhetoric, attempting to sway voters, and perhaps the general populus, in favor of certain ideologies, candidates, or movements. These practices will undoubtedly raise serious ethical concerns, and in some cases, where agents are used to manipulate voters, spread political disinformation, or incite divisiveness, strict ethical and legal boundaries will need to be established and enforced.
• Proliferation of Synthetic Relationships: Humans will inevitably attribute human-like qualities to advanced AI agents, particularly as they spend more time interacting with them. This wouldn’t be as serious a concern if people were able to realize that synthetic human-AI relationships shouldn’t be treated as surrogates for genuine human relationships; this concern is also exacerbated by the fact that AI’s personalization capabilities will only continue to improve, cultivating a user-centric illusion that a model understands you not only better than your friends and family, but better than yourself. Psychologically vulnerable individuals will be most at risk here, but this only adds another layer to the scalability component of this concern — mental health, particularly among young populations, has been steadily declining over the last decade, and younger generations tend to more readily adopt emerging technologies.
• Erosion of Traditional Gate-Keepping Institutions: Current AI agents are already enormously powerful tools for expanding knowledge and creativity, accelerating learning, and helping users discover new ideas, concepts, or skills. As more people recognize and unlock this potential, the institutions that traditionally provide, harbor, and guide knowledge will be forced to confront a threat to their continued relevance and utility in society. This will cause a paradigmatic shift in how knowledge is communicated, stored, and accessed at the institutional level, and it may lead in one of two directions: 1) the decentralization of knowledge-based institutions via democratized AI-powered information sharing platforms, or 2) an even more intense gatekeeping structure where institutions treat their knowledge not as a public resource, but as a proprietary and highly secured one.
• Continuous Societal Experimentation: As regular people become more accustomed to and comfortable with using advanced AI throughout their daily lives and professions, experimentation will emerge as a natural byproduct of sustained use and human curiosity. People won’t just use AI to resolve an immediate problem or achieve a necessary task — they’ll begin venturing outside their comfort zone, testing its limits to reveal what else it might be capable of. More curious users could even begin orchestrating capabilities and adversarial tests of their own, becoming a potent collective force and resource for improving AI safety and ethics.

Which Future Do We Want to Live In?

We’ll conclude by presenting two visions of the future, each illustrated by a set of assumptions:

Version 1 (Optimistic)

Core assumptions:

• Governments will establish agent-specific regulations that set strict boundaries for protecting and preserving human autonomy and dignity, particularly in socio-economic contexts.
• Businesses will offer regular upskilling and retraining opportunities and resources, constantly working to preserve their workforce and ensure their continued relevance as AI advances.
• Those who lose their jobs due to AI automation will receive government subsidies and unemployment benefits for an extended period while figuring out how to pivot their careers and livelihoods.
• Academic institutions will integrate AI education as a core requirement within their curricula, focusing on teaching students how to create value and maintain critical thinking and creativity in an AI-enabled future.
• Leading AI labs will substantially increase funding for red teaming practices, expanding them across multiple domains and disciplines, while honing in on efforts to contain and counteract large-scale, AI-powered adversarial threats.
• Digital information hubs, search engines, and social media platforms will develop and implement reliable mechanisms for validating AI-generated content with clear disclaimers.
• Internationally, AI arms race dynamics will subside, with nations that are leading AI innovation establishing an internationally binding pact that aims to control for the possibility of AI-induced mutually assured destruction.
• Governments will ban the use of AI for mass surveillance, manipulation, profiling, social credit scoring, and predictive policing purposes, prioritizing fundamental human rights above all else.
• Scientific research and development will accelerate beyond what we can imagine, with teams of humans and AIs rapidly developing novel renewable energy technologies, disease treatments and cures, and material and industrial engineering innovations.
• Where AI is leveraged for decision-making in critical sectors, all institutions will be held to strict transparency and explainability standards whereby all AI-driven decisions are easily accessed and understood by those affected.
• AI will democratize knowledge in ways that make the internet seem infantile, helping populations around the world gain personalized access to new skills, information, and learning approaches.
• At the cultural level, people will realize that AI isn’t a surrogate for human social relationships and connectedness, but instead, a mechanism through which to better understand one another.

Version 2 (Pessimistic)

Core Assumptions:

• Early AI adopters will capitalize on their skill-based advantages and create enormous, potentially inconceivable wealth for themselves, establishing a new societal ruling class that’s virtually impossible to catch up with.
• The “AI aristocrats” will use their wealth to monopolize existing industries, pocket politicians, and further consolidate their power, effectively ruling from “behind the scenes” while in broad daylight. This will lay the stage for a fully-fledged technocracy.
• Frontier AI labs will prioritize accelerationism, deploying increasingly advanced models without adequate safety and ethics testing. These models will remain deeply prone to weaponization by bad actors.
• To win the AI arms race, rogue, non-state actors will attempt to infiltrate leading AI labs and government/research institutions to either replicate and weaponize their technologies or enfeeble/collapse the infrastructures that support continued development.
• Governments will abandon all efforts to regulate AI, justifying their decision by explaining that AI simply moves too fast for regulation to keep up or prove effective. They’ll also make no efforts to subsidize or counteract AI-driven job loss.
• Governments will employ AI for mass surveillance, profiling, predictive policing, and social credit scoring, claiming that such practices are necessary to sustain an AI-enabled future.
• Large-scale AI-generated disinformation campaigns will be used to seed divisive rhetoric at the population scale, manufacture false political and scientific narratives, and elicit violent uprisings within destabilized or vulnerable communities.
• Scientific institutions will be pressured into government or private contracts that require them to use AI to build weapons of mass destruction, like weaponized pathogens and precision drone swarms. These contracts will also address human-AI symbiosis and eugenic initiatives that focus on altering and enhancing the human genome.
• Across critical sectors, AI-driven decisions will only become more opaque, and this opacity will be excused under the “black box” argument. Millions will be denied access to critical goods and services, and they will never know why.
• Advanced AI technologies will only be accessible to the hyperwealthy, and AI labs will justify these monumental price increases by claiming that it’s the only way for them to sustain AI training and infrastructure development costs.
• Academic institutions will resist any form of AI adoption, and they will insist that AI in the educational context can only be seen as a force of intellectual destruction. This will leave young generations entirely unprepared for an AI-enabled future.
• Across cultures, people will develop insecure and unhealthy emotional bonds and attachments with AIs, leading to self-isolation, psychological vulnerability, and the collapse of social cohesion at scale.

Both of our visions represent idealistic extremes, and they’re incomplete (we could make hundreds of assumptions about what the future might look like). Still, in reality, what we expect may fall somewhere in between these two visions. Nonetheless, when thinking about this future, we offer readers one crucial piece of advice: don’t assume that because you live in a democracy, you can expect the future to unfold in a way that benefits you.

If you find our content useful and interesting, consider following our blog, where you can find all our other deep dive series along with additional content on AI governance, risk management, safety and ethics, literacy, and innovation. If you’re interested in exploring our experimental insights and conclusions, consider checking out our AI experiments, which are published weekly.

By contrast, if you’ve already commenced your AI governance and risk management journey and you’re encountering difficulties or simply wish to enhance and scale your strategies, we invite you to try Lumenova’s RAI platform and book a product demo today.

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Related topics: AI Agents