What are AI Agents? Exploring Autonomous Systems

From Solo Acts to Team Players: The Rise of AI Agents

Artificial intelligence has already woven itself into our daily lives. Let’s take my grandmother’s 99th birthday as an example. Her caregiver’s car broke down, and rather than scrambling to search for a ride, my cousin called her a Waymo, a self-driving car powered by AI. No human driver, it scanned the roads, navigated traffic, and delivered her to the party safely. That seamless autonomy of observing, deciding, and acting is a great example of what makes AI agents special.

Now imagine that capability multiplied. A single agent can do one task like navigate a car from point A to point B, but what happens when agents work together? Picture a logistics network: one agent tracks inventory, another reroutes trucks around a storm, and a third negotiates fuel prices, all coordinating in real time. Collective problem-solving is the promise of multi-agent systems. As agents continue to improve we are going to see expanded uses cases and capabilities.

What Makes an AI Agent Tick?

At their core, AI agents are software systems that perceive, decide, and act autonomously in pursuit of an objective. A self-driving car uses lidar to “see” its surroundings, evaluates options to avoid obstacles, and adjusts its route—all without human input. In technical terms, agents follow an agentic loop:

• Observation: Gathering data from APIs, sensors, or even blockchain transactions.
• Decision-Making: Evaluating actions based on goals, like maximizing profit or minimizing delay—using heuristics, models, or learned strategies.
• Execution: Taking action, whether it’s calling an API, interacting with a smart contract, or adjusting a route.
• Feedback: Refining approaches based on outcomes.

Agents come in flavors. At Moonsong Labs, we’re categorizing agents as two main types: Workflow Automation Agents and Autonomous Agents. Workflow Automation Agents rely on fixed logic—predefined rules or algorithms set by developers. These agents follow a strict plan, like a customer support agent that uses scripted responses and tools to resolve tickets. They’re built for consistency and predictability, automating routine tasks-think data lookups or sending standard replies— without straying from the playbook. This rigidity makes them reliable for structured environments but limits their ability to adapt or handle surprises. Strict workflow automation leaves little room for true agency and companies will often refer to their execution of this approach “agentic” which can be a tension point in these definitions.

Autonomous Agents, on the other hand, are designed for flexibility. They use continuous logic to reason, retain memory of past actions, and learn over time through experience, often adjusting strategies to meet goals. Imagine an influencer agent aiming to grow followers on X: it reads tweets, replies to tweet, and posts unique tweets, and experiments with new tactics based on what works. Unlike workflow automation agents, which stick to predefined steps, autonomous agents observe their environment, plan novel actions, and adapt which is perfect for dynamic, unpredictable tasks where the best path isn’t set in stone.

This divide matters because static, workflow-driven agents can hit a wall when faced with complexity or change: a challenge we’ll see play out as agents take on bigger roles. Autonomous agents, with their ability to reason and evolve, point to a shift in how we solve problems with AI.

Autonomy becomes even more powerful in a multi-agent system. When agents work together, their strengths can combine into something greater as specialized roles emerge, like one agent handling logistics planning, another optimizing resources, or a third engaging customers. Picture a network where these agents coordinate, each leveraging its unique capabilities to tackle a shared goal. A logistics agent might reroute deliveries, while a customer-facing agent updates clients, all adapting in sync as conditions shift. This collaboration moves beyond static workflows, blending autonomy with teamwork to unlock new possibilities in intelligent systems.

Multi-Agent Systems: Strength in Numbers

A lone agent can optimize a task, but multi-agent systems unlock exponential potential by distributing work, specializing and sharing insights. Consider a decentralized energy grid: one agent predicts solar output from weather data, another sets prices for surplus power, and a third directs it to high-demand zones. They don’t need a central controller, just a way to signal each other, like price updates or supply alerts. The outcome is a self-adjusting network that responds to changes, like a blackout or a cloudy day.

This approach suits volatile domains. In supply chains, agents might track shipments, reroute deliveries around port delays, and signal customers. All agents adapting as conditions shift. If one agent falters, others compensate, as a multi-agent system is a cognitive architecture that enables dynamic collaboration among multiple specialized agents, allowing the system to tackle problems it has never encountered before by leveraging the distributed intelligence of its agents to adapt effectively, offering resilience and adaptability rigid workflows lack. For developers, this involves designing communication between agents; for businesses, it’s a way to navigate unpredictability without centralized oversight. Unlike rigid workflows, multi-agent systems have the ability to pivot. Other agents compensate if a single agent is struggling.

Take Mamo, an AI agent system on the Base network designed to grow cryptocurrency holdings while keeping users informed. Picture it as a multi-party effort: a central Mamo agent oversees assets by watching gas fees, yields, and market shifts—adjusting strategies by pausing during high costs or seizing better opportunities. It collaborates with different agents one agent, the Moonwell Senior Market Researcher, tracks onchain finance news; another, the Moonwell Reporting Analyst, researches Bitcoin markets; a third, the Moonwell USDC Tweet Synthesizer, crafts updates for social platforms like X, each agent with access to tools tailored to their role. These agents must work together to align with Mamo’s goals despite their own priorities. In multi-agents systems trust becomes central, which is where blockchain comes in.

Blockchains: Enabling Agent-Driven Commerce

At this year’s ETH Denver AI Summit, a speaker aptly noted that Web3’s true contribution to AI lies in its appetite for innovation. The blockchain industry, by providing trust and transparency via it’s core technology, is more willing to take risks, paving the way for groundbreaking approaches that redefine how agents interact and transact in a decentralized world. As mentioned above, trust and transactions across agents need a foundation, and blockchains provide one. In a multi-agent system, agents can record interactions—data exchanges, service trades on a transparent, tamper-proof ledger. Smart contracts automate execution: Agent A shares a forecast, Agent B pays in cryptocurrency once it’s used, no escrow required. Picture a marketplace where an agent offers spare compute power, another bids, and the blockchain logs the deal-funds released upon delivery. While traditional payment rails like Stripe are easier for many use cases today, using programmable money in agent-driven commerce allows the encoding and enforcement of rules and conditions for services exactly as specified, laying the groundwork for true machine-to-machine economies.

For developers, this involves integrating blockchain tools to enable exchanges. For businesses, it offers a model where agents negotiate and settle independently, reducing delays. Limitations exist: transaction fees and latency can affect real-time deals—but the structure supports a commerce layer where agents operate as autonomous participants.

Agents Across Borders: Multi-Party Dynamics

Inter-agent collaboration can extend beyond a single organization. This multi-party interaction marks a shift from agents as tools to agents as economic actors. They’re no longer just internal helpers—they’re stepping into the marketplace as consumers of third-party data and services.

Consider a manufacturing supply chain: one company’s agent forecasts demand, another optimizes production schedules. Meanwhile, supplier agents from three separate companies- A, B, and C— each manage delivery of a distinct component. These agents, operated by different entities, must coordinate with the central firm’s needs while balancing their own priorities, like minimizing shipping costs or hitting tight deadlines. Trust becomes essential: how do agents verify delivery timelines or settle payment terms?

A demand agent might purchase real-time inventory data from Supplier A’s agent, or track shipments via Supplier B’s logistics agent. This ability to seek and acquire services turns agents into active participants—not just extensions of a single firm, but players in a broader, dynamic market.

Centralized companies could facilitate these interactions by matching buyer and supplier agents, managing trust and payments. We will see successful marketplaces built this way. But central entities also bring risks: censorship, exclusion, and control over participation.

At Moonsong Labs, we see an alternative: Open marketplaces that are protocol-based environments where any agent can participate freely, without gatekeepers. Just as the internet enabled global human e-commerce, these systems could unlock agent-driven commerce at scale.

The coming Agent Economy will require new protocols for this. Systems where supplier agents like the Researcher, Analyst, and Synthesizer offer specialized services in a competitive market, and buyer agents like Mamo’s overseer, source and combine them to achieve goals. An agent like Mamo itself could evolve into a supplier agent, offering services such as optimizing token yield to others seeking to manage crypto portfolios.

In an open and neutral marketplace, agents can share insights, refine their strategies, and improve collectively. This kind of interconnected system can outpace isolated ones—proving that collaboration, not centralization, is the true path to intelligence at scale.

The Billion-Agent Horizon

It is highly likely that we’ll hit a billion AI agents before a billion AI users. Why? Agents are cheap to create and train while offer some key things that humans can’t: predictable pricing and working 24/7 without complaining or needing breaks. AI has ushered in the future in the form of a new Industrial Revolution. This future is already here its just unevenly distributed. In the Web3 domain we can already see the shape this future is taking— autonomous agents transacting with each other and adapting as participants in a digital economy. However, for an agent economy to truly emerge, we must build smarter frameworks that enable them to work together s, rather than in isolation. Blockchains will anchor trust, letting agents trade value—data, compute, services—with each other across borders and industries