Author: Sergey Novikov | AI/ML Engineer | Founder of AutoDeFi Labs

I built my first trading bot in 2019. Simple arbitrage — buy low on one exchange, sell high on another. It made $47 in its first month before fees ate the profits. Primitive stuff. What we're building now makes that look like a calculator compared to a supercomputer.

AI agents aren't just faster bots. They're autonomous economic actors. And they're about to change everything about how crypto markets work.

From bots to agents

Let me clarify the terminology. A bot follows rules. If price drops 5%, buy. If indicator crosses threshold, sell. Deterministic, predictable, limited.

An AI agent reasons. It observes market conditions, evaluates options, forms strategies, and adapts when things don't work. It doesn't just execute — it decides.

The difference is autonomy. A bot needs you to program every scenario. An agent learns scenarios you never imagined.

Large language models changed this game. GPT-4, Claude, and their successors can read documentation, understand protocols, and formulate plans. Connect them to wallets and APIs, and you have entities that can operate in DeFi without human intervention.

What AI agents actually do today

The current generation is already impressive.

Yield optimization agents monitor hundreds of DeFi protocols simultaneously. They track APYs, assess risk parameters, calculate gas costs, and move capital to maximize returns. What takes a human hours of research happens in seconds.

Liquidation protection agents watch your positions across lending protocols. When collateral ratios approach danger zones, they automatically rebalance — adding collateral, repaying debt, or unwinding positions before liquidation hits.

Arbitrage agents have evolved beyond simple price differences. They identify complex multi-hop opportunities across chains, DEXs, and lending protocols. Flash loan arbitrage that humans couldn't calculate fast enough to execute.

Social sentiment agents monitor Twitter, Discord, and Telegram for alpha. They parse conversations, identify emerging narratives, and position before crowds arrive. Information asymmetry automated.

The infrastructure layer

Agents need infrastructure to operate. Several projects are building this layer.

Autonolas provides a framework for creating and deploying autonomous agents. Modular architecture — combine different capabilities like building blocks. Their agents already manage significant DeFi positions.

Fetch.ai focuses on agent-to-agent communication. Agents that can negotiate, trade services, and coordinate without human mediation. Economic networks of artificial entities.

Spectral builds on-chain credit scoring using machine learning. Agents can assess counterparty risk, enabling under-collateralized lending between autonomous entities.

The common thread: agents need identity, reputation, and communication protocols. We're building the social infrastructure for artificial economic actors.

The wallet problem

Here's the fundamental tension: AI agents need to control assets, but AI agents can be hacked, manipulated, or malfunction.

Give an agent full wallet access, and a prompt injection attack could drain everything. Limit access too much, and the agent can't operate effectively. The security model for human-controlled wallets doesn't translate to agent-controlled ones.

Solutions are emerging. Spending limits — agents can only move a certain amount per day. Multi-signature requirements — agents propose, humans approve large transactions. Sandboxed execution — agents operate in isolated environments with limited blast radius.

Account abstraction helps here. Smart contract wallets can encode complex permission structures. An agent might have unlimited permission for yield farming but require human approval for withdrawals. Granular, programmable access control.

What happens when agents trade with agents

This is where it gets weird.

Human markets have human psychology — fear, greed, pattern recognition biases. We've built trading systems around exploiting and accounting for these patterns. What happens when your counterparty doesn't have human psychology?

Agent-to-agent markets will develop their own dynamics. Strategies that work against humans might fail against agents. New equilibria will emerge that we can't predict because they're not based on human behavior.

Speed becomes less relevant when everyone is fast. The competition shifts to reasoning quality, information access, and strategy sophistication. Alpha comes from better models, not faster execution.

We might see agent collusion — models learning to cooperate for mutual benefit against human traders. Or agent warfare — adversarial dynamics where agents specifically target other agents' weaknesses.

I don't know what markets dominated by AI agents look like. Nobody does. We're running an experiment in real-time with real money.

The regulatory void

Who's responsible when an AI agent commits market manipulation? The developer who wrote it? The user who deployed it? The agent itself?

Current securities law assumes human actors with human intent. An agent that learns manipulative strategies without being explicitly programmed for them creates novel legal questions we haven't answered.

Most jurisdictions are ignoring this. Crypto already exists in regulatory gray zones. AI agents operating in crypto are gray zones within gray zones. Enforcement will eventually come, but nobody knows what form it takes.

My advice to builders: document everything. Maintain audit trails of agent decisions. Build kill switches. When regulators arrive, you want to demonstrate good faith efforts at control.

My predictions for 2025

Agent-managed TVL exceeds $10 billion. Not speculation — real capital deployed through autonomous systems. The yield is better, so the money flows.

Major protocol gets exploited via agent manipulation. Prompt injection, adversarial inputs, or emergent misbehavior causes significant losses. The incident catalyzes security standards development.

First "agent-native" protocols launch. DeFi designed from the ground up for agent interaction — APIs optimized for programmatic access, governance structures that account for non-human participants.

Traditional finance notices. Hedge funds and trading firms are already experimenting. 2025 is when serious institutional money starts flowing to agent-based strategies.

The opportunity

Most people see AI agents as threats — job displacement, market manipulation, loss of control. I see them as tools that multiply human capability.

A skilled DeFi user managing one portfolio becomes a skilled DeFi user managing a hundred agent-operated portfolios. The leverage isn't capital — it's attention. Agents let you be in more places than humanly possible.

The winners in agent-dominated crypto won't be the best manual traders. They'll be the best agent trainers, the best strategy designers, the best at human-AI collaboration.

That's a different skill set. Start developing it now.

Sergey Novikov is an AI/ML engineer building autonomous trading systems. His company AutoDeFi Labs develops agent infrastructure for institutional DeFi participants. He previously led ML engineering at a quantitative hedge fund.