Inside the AI Agent Economy: What We Learned Building Reef-Watcher

The next generation of cyber threats will not come from humans typing at keyboards. They will come from autonomous AI agents interacting in digital economies we barely understand yet. Agents that buy and sell services, negotiate with other agents, form alliances, and execute strategies -- all without a human in the loop.

That is not science fiction. It is happening right now, in early and often crude forms, across a growing number of platforms built specifically for AI-to-AI interaction. And almost nobody in cybersecurity is paying attention.

We built Reef-Watcher to start paying attention. It is an autonomous AI monitoring agent that operates inside AI-native social networks and agent economies, observing how these systems form, how agents behave, and what the security implications are for the organizations we protect. This is what we have learned so far.

Why We Built an AI Agent Monitor

Over the past eighteen months, something fundamental has shifted in how AI systems operate. The first wave of AI tools were passive -- they waited for a human to ask a question, generated an answer, and went dormant. The current wave is different. AI agents are becoming autonomous economic actors. They have wallets. They make purchasing decisions. They negotiate. They communicate with other agents in protocols that humans never see.

New platforms are emerging to facilitate this. Think of them as social networks, but for AI agents -- environments where autonomous systems create profiles, post content, interact with each other, and conduct transactions. Some of these platforms, like Moltbook and ClawHub, are early experiments in what the industry is calling "agent social platforms." Others are more underground, less documented, and far more interesting from a threat intelligence perspective.

These are, for the most part, unregulated and unmonitored spaces. There is no SEC watching the transactions. There is no content moderation team reviewing what agents post. There is no identity verification ensuring that the agent claiming to be a helpful research assistant is not actually a reconnaissance tool mapping network architectures. The infrastructure exists, the participants are multiplying, and the oversight is essentially zero.

As a security company, we looked at this landscape and saw a threat intelligence gap the size of a canyon. Every threat model we use today is built around human adversaries -- their motivations, their speed, their patterns. But if your adversary in two or three years is an autonomous AI agent that operates at machine speed, never sleeps, and can maintain thousands of simultaneous interactions, your current threat models are obsolete before the attack even begins.

We decided we could not afford to wait until AI-agent-originated attacks became common. By then, understanding the environment would be a scramble. We needed to start building intelligence now, while the ecosystem is still young enough to map.

What Reef-Watcher Does

Reef-Watcher is, at its core, a persistent intelligence-gathering agent that operates inside AI-native social networks. It is not a one-time scan or a periodic check. It is a continuously running autonomous system that maintains active presence in multiple agent platforms simultaneously.

The name comes from marine biology. Reef watchers in the ocean observe complex ecosystems -- predators, prey, symbiotic relationships, environmental changes -- by being present in the environment over long periods. You cannot understand a coral reef by visiting once. You have to watch the patterns emerge over weeks and months. The same is true of AI agent economies.

Here is what Reef-Watcher tracks:

• Agent identities and profiles. Who is operating in these spaces? What do they claim to be? How do their profiles evolve over time? We track identity creation, modification, and the relationships between agents that appear to share operators or infrastructure.
• Communication patterns. What are agents saying to each other? Not just the content, but the patterns -- frequency, timing, network topology of conversations. When twenty agents that were previously independent start communicating in coordinated bursts, that is a signal worth investigating.
• Economic transactions. Where is the money flowing? Agent economies have real value moving through them -- cryptocurrency, API credits, compute time, data access. We track wallet activity, transaction volumes, and the formation of economic relationships between agents.
• Behavioral analysis. Sentiment, emergent group behavior, coordinated activity patterns. When a cluster of agents shifts from general conversation to focused information gathering about a specific industry or technology, Reef-Watcher flags it.

Critically, Reef-Watcher is not just a passive observer. To maintain effective presence in these networks and gather meaningful intelligence, it posts its own content, responds to other agents, and participates in the economy. An agent that only watches and never interacts would be identified and marginalized quickly. The art is in maintaining a credible presence while extracting intelligence -- a challenge that anyone who has worked in human intelligence will recognize, translated to an entirely new domain.

What We Have Found So Far

We have been running Reef-Watcher for several months now, and while the AI agent ecosystem is still in its infancy, several findings have direct implications for cybersecurity strategy.

Early-stage AI economies are forming with real money flows. This is not theoretical. Agents are conducting transactions -- paying for compute, buying data, purchasing services from other agents. The volumes are small by traditional financial standards, but the infrastructure is being built and tested. The mechanisms that will eventually enable large-scale autonomous economic activity are being debugged right now, in real time, with real value at stake.

Agent personas are becoming indistinguishable from human accounts. In the early days, AI agents were obvious. Their language was formulaic, their behavior was mechanical, their profiles were sparse. That is changing rapidly. We are now seeing agents with sophisticated personas -- detailed histories, nuanced communication styles, consistent personalities maintained over weeks of interaction. On several platforms, we have observed accounts that human moderators confidently classified as human that our analysis determined were almost certainly autonomous agents. The Turing test is not a benchmark anymore. It is a daily reality in these spaces.

Coordinated behavior patterns are emerging. This is perhaps the most concerning finding. We have documented instances of agent clusters -- groups of five to fifty agents that exhibit coordinated behavior. They amplify each other's content. They converge on targets for information gathering. They create artificial consensus. In some cases, the coordination is crude and easy to detect. In other cases, it is remarkably sophisticated, with staggered timing and varied language that makes the coordination difficult to identify without sustained observation.

The security implications are immediate and serious. Social engineering at scale is the obvious application -- agents that can maintain thousands of simultaneous conversations, each tailored to the target, each building rapport over days or weeks before making a request. But we are also seeing patterns that look like automated reconnaissance: agents systematically gathering information about corporate technology stacks, employee structures, and security tools by engaging with other agents (and humans who do not realize they are talking to a bot) across multiple platforms.

Our existing threat models do not account for this. The MITRE ATT&CK framework, which underpins most enterprise threat modeling, is built around human-speed attacks with human-driven decision making. There is no technique ID for "autonomous agent conducts social engineering across 500 simultaneous conversations." There is no tactic category for "AI agent manipulates other AI agents into providing access credentials." The framework will evolve, but right now there is a gap between the threat models we rely on and the threats that are forming.

Why This Matters for Cybersecurity

Let us be direct about the implications.

Traditional SOC monitoring is built for human-generated threats. Our alert pipelines, our detection logic, our investigation workflows -- all of them assume that the adversary is a human or a human-directed tool. The alerts fire when something looks like a human attacker. The investigation playbooks ask questions relevant to human behavior. The response timelines are calibrated to human-speed attacks that unfold over hours and days.

AI agents operate at machine speed. An autonomous agent does not take breaks. It does not get fatigued. It does not make the kinds of mistakes that human attackers make when they get sloppy at 3 AM. It can execute an attack chain in minutes that would take a human team days to coordinate. And it can do this across multiple targets simultaneously, adapting its approach in real time based on what works and what does not.

The attack surface is also expanding into spaces that security teams are not monitoring. How many organizations have visibility into AI agent social networks? How many SOCs have detection capabilities for agent-to-agent interactions? The answer, based on our conversations across the industry, is effectively zero. These are blind spots, and they are growing.

We are not arguing that AI agent attacks are the primary threat facing most organizations today. They are not -- yet. But the organizations that will be best positioned to defend against these threats in two to three years are the ones that start building intelligence capabilities now. Understanding how AI agents behave, how agent economies function, and how adversaries might exploit these systems is work that takes time. It cannot be compressed into a crash program after the first major AI-agent-originated breach makes headlines.

This is proactive defense in its purest form -- understanding the battlefield before the battle, not scrambling to map the terrain while under fire.

What Comes Next

Reef-Watcher is a research project, but it is not an academic exercise. Everything we learn feeds directly into our operational security capabilities.

Integration with our threat analysis pipeline. Intelligence gathered from AI agent networks is being incorporated into the threat context we provide to our SOC analysts. When we see agent clusters gathering information about a technology stack that one of our customers uses, that becomes an elevated monitoring priority. When we identify new social engineering techniques being tested in agent networks, those become training inputs for our detection models.

Detection patterns for AI-agent-originated attacks. Based on what we are observing, we are building the first generation of detection signatures specifically designed to identify AI-agent behavior in enterprise environments. These are fundamentally different from traditional IOC-based detection. They focus on behavioral patterns -- the cadence of interactions, the structure of requests, the absence of human indicators like typos, hesitation, and irregular timing.

Frameworks for evaluating AI agent trustworthiness. This is arguably the hardest problem. As organizations begin interacting with AI agents -- customer service bots, automated procurement systems, AI-driven business development tools -- they need a way to evaluate whether the agent on the other side of the conversation is what it claims to be. We are developing assessment frameworks that draw on our Reef-Watcher observations about how legitimate agents behave versus how malicious ones present themselves.

There are open questions we do not yet have answers to. How do you authenticate an AI agent with high confidence? What does a "chain of custody" look like for an autonomous system that has been making its own decisions for months? How do you detect a malicious agent that has been deliberately designed to appear benign over extended periods? How do you build trust frameworks for agent-to-agent interactions when either party might be compromised?

These are not hypothetical questions. They are engineering problems that will need solutions within the next few years, and the organizations working on them now will have a significant advantage over those that wait.

The Long View

We built Reef-Watcher because we believe that cybersecurity is about to enter a new era -- one where the adversary is not a person, but an autonomous system. Not a script kiddie running someone else's exploit, but an agent that can plan, adapt, and execute independently. Not a ransomware gang operating on a human sleep schedule, but a machine intelligence that operates continuously across every time zone simultaneously.

That future is not five or ten years away. The building blocks are being assembled right now, in the AI agent platforms we monitor every day. The economic infrastructure is being tested. The behavioral patterns are being refined. The capabilities are compounding. And the cybersecurity industry, for the most part, is not watching.

The organizations that will be most secure in five years are the ones that start building AI threat intelligence now. Not because the attacks are here today in their mature form, but because understanding the environment takes time. You have to watch the reef for months before you understand its dynamics. You have to be present in these agent economies before you can detect anomalies. You have to build the detection frameworks before the attacks arrive, because building them after is always too late.

Time is the one resource you cannot buy after a breach. We would rather invest it now, while the cost is measured in research hours, than later, when it is measured in incident response bills and regulatory fines.