What this lesson solves

The first two parts built the foundations of AI and Web3 separately. This lesson begins the actual overlap. Once an AI system can do more than generate text—once it can read onchain state, call tools, plan actions, and trigger transactions—it starts to resemble an onchain agent.

What an agent is

An agent is not just a model answering one prompt.
It is a system organized around a goal, usually with at least four components:

• a goal
• perception of relevant data or environment state
• planning
• execution through tools or actions

If a system only answers questions but cannot carry context forward and act, it is usually not a full agent.

Why blockchain makes agents more interesting

In normal software environments, agent actions often stay inside closed systems.
Blockchain changes the picture because:

• assets can be managed programmatically
• state changes can be publicly verified
• execution history is traceable
• many protocols expose standard interaction surfaces

That means an agent is no longer limited to giving advice. It may participate in actual onchain operations.

A basic onchain agent workflow

A typical onchain agent can be described in five steps:

1. read information: wallet state, protocol data, prices, user constraints
2. interpret context: identify conditions, risks, and opportunities
3. generate a plan
4. call tools: query contracts, build transactions, request signatures
5. execute and monitor results

The hard part is rarely the prompt. The hard part is connecting reasoning, permissions, and execution safely.

Three common roles for onchain agents

Research and information synthesis

Examples:

• summarizing governance proposals
• tracking protocol changes
• analyzing address behavior
• monitoring market events

These are relatively low-risk because they mostly produce information.

Decision support

Examples:

• recommending allocations
• flagging risk exposure
• suggesting rebalancing plans

These do not necessarily move assets directly, but they influence decisions.

Automated execution

Examples:

• triggering rebalancing
• interacting with lending protocols
• coordinating cross-protocol actions
• completing payment flows

These are the highest-risk cases because they move from interpretation to direct change in the world.

Why permission boundaries matter more than intelligence

The most important design task in an onchain agent is not making it smarter. It is preventing it from doing what it should not do.

At minimum, the system should define:

• which actions are advisory only
• which require explicit confirmation
• which are allowed to run automatically
• how failure stops or rollbacks are handled

Without these boundaries, stronger agents simply create stronger failure modes.

A practical architecture split

A safer structure usually looks like this:

• model layer: understanding, planning, strategy generation
• tool layer: reading onchain and offchain data
• execution layer: signing, sending transactions, calling contracts
• rule layer: whitelists, amount limits, risk thresholds, permission checks

This lets AI stay flexible while execution remains bounded.

An AI × Web3 example

Suppose a user says: “Allocate my stablecoins across low-risk strategies.”

An onchain agent might:

• read the wallet state and current approvals
• fetch yields and protocol risk signals
• generate several candidate plans
• select one under user-defined constraints
• request confirmation
• execute transactions through constrained rules

The real product value is the full chain of interpretation, planning, and execution—not a single answer.

Common risks

Typical risks include:

• wrong data interpretation
• failed tool calls and inconsistent intermediate state
• excessive permissions
• incorrect cost estimates leading to high gas or slippage losses
• fast risk propagation across protocol dependencies

Minimum takeaway

After this lesson, you should be able to explain:

• the difference between an agent and a one-shot model interface
• why blockchain makes agent execution more meaningful
• the basic workflow of an onchain agent
• why permission boundaries matter more than raw intelligence

What comes next

The next lesson focuses on AI + DeFi and looks at what happens when agents meet lending, swaps, yield strategies, and risk management.

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