Agent

About 1234 wordsAbout 4 min

2026-05-12

An Agent is an AI system that can continuously call tools, read state, and adjust steps around a goal. The key is not whether it is "human-like," but whether it can move from suggestion to action inside clear permissions and an auditable process.

Why Learn This

When an LLM answers a question once, it is usually only generating text. An Agent goes further: it can break down tasks, look up material, call APIs, write code, generate operation drafts, wait for feedback, and then continue to the next step.

That is also the real risk point of Agents. Once an Agent can call tools, write into systems, submit requests, or trigger payments, errors are no longer just "wrong answers"; they become "wrong actions." So the core of Agent design is not making the model more human-like, but giving the execution loop clear boundaries.

The point of learning Agents is not to chase frameworks, but to establish division of labor: the model proposes candidate actions, the system limits the action space, and the user approves high-risk boundaries.

First Principles

An Agent is not autonomy itself; it is a constrained execution loop. Goal, tools, state, permissions, and stop conditions are all required.

An Agent with only a model and tools is like a script runner that can talk. A usable Agent must know what it can do, what it cannot do, how to verify completion, how to stop on failure, and who can audit what it has done.

Tools are more dangerous than answers: reading data, writing to a database, sending requests, and changing configuration are not the same risk level.
State must be externalized and queryable: task progress, tool results, failure reasons, and user confirmations should be recorded, not hidden only in model context.
Stop conditions must be explicit: reaching the goal, exceeding budget, lacking information, crossing risk boundaries, or user rejection should all stop the Agent.

Knowledge Nodes

Tool Use

Tool Use means the Agent calls external capabilities: search, databases, APIs, code execution, email, payment interfaces, internal systems, and more.

Tools turn an Agent from "can answer" into "can do." But tools also make Prompt Injection and model misjudgment dangerous. An Agent that can read webpages and is influenced by a malicious webpage is very different from an Agent that can write into a system and is influenced by that webpage.

Tool design should make these clear:

input schema
permission scope
whether it is read-only
whether it creates external side effects
how calls are logged before and after execution
which calls require human confirmation

Planning

Planning breaks a goal into steps. For example, "help me analyze this DAO proposal" can become: read the proposal, retrieve historical discussion, summarize disputes, check the voting mechanism, and generate a risk checklist.

Plans are useful, but they should not be mythologized. A plan generated by the model is only a candidate route, not authorization. The closer it gets to high-risk action, the more the plan must be broken apart and checked by system rules.

A good Agent plan should expose:

which tool each step needs
whether each step reads or writes
which steps can run automatically
which steps require user confirmation
whether failure can be retried
how final task completion will be verified

State

State is the current task state of the Agent, including user goal, completed steps, tool returns, errors, budget, confirmation records, and final output.

Many Agent demos keep state only in prompt history. That is not enough. Production systems need queryable, recoverable, auditable state. Otherwise it is hard to answer: why did the Agent call this tool? Did it already receive user confirmation? At which step did it drift from the goal?

In scenarios with external execution, state should also record environment, version, key parameters, tool-call results, confirmation requests, and revocation events.

Reflection

Reflection asks the Agent to check its intermediate results, such as noticing insufficient information, tool failure, or an unreasonable plan, and then correcting the next step.

It can improve the quality of complex tasks, but it cannot replace external verification. Agent self-reflection is still performed by a model, and a model may rationalize its own errors. Especially for writes, approvals, and payments, reflection can only assist diagnosis; it cannot be the final safety judgment.

Self-checking can improve quality; deterministic checking is what can carry risk.

Multi-Agent

Multi-Agent means several Agents divide work, such as a research Agent reading material, a development Agent writing code, a security Agent reviewing risk, and an execution Agent calling tools.

It fits complex workflows, but it also amplifies coordination problems: lost context transfer, unclear responsibility boundaries, one Agent treating another Agent's mistake as fact, and tool permissions spreading across roles.

When building Multi-Agent systems, ask a plain question first: do multiple Agents really reduce complexity? If you only split one unclear process into multiple unclear roles, the system becomes harder to debug.

Where It Fits in AI x Web3

Agents sit between model capability and on-chain execution. They can advance user goals into multi-step workflows, but they cannot bypass account, permission, and settlement rules.

A relatively stable AI x Web3 Agent architecture usually looks like this:

The user provides a goal and constraints.
The Agent reads context and generates a plan.
The system splits the plan into read-only steps and candidate write steps.
Read-only tools run automatically; write tools enter policy checks.
Simulation shows on-chain impact.
The user confirms high-risk actions.
Wallet / Smart Account executes.
Logs record each step and final state.

The most dangerous design is giving an Agent vague goals, broad tools, long-term memory, and large-asset permissions at the same time.

Minimum Practice

Build a minimal "DAO proposal research Agent."

It can only perform read-only actions:

read the proposal text
retrieve forum discussions
summarize supporting and opposing arguments
mark missing information
output a pre-vote checklist

Do not let it vote directly. Instead, require the output to clearly state:

which sources were used
which conclusions lack enough evidence
whether governance or funding risks were found
what the user still needs to manually check before voting

After that, design an upgraded permission version: only after the user explicitly authorizes it, and after vote-transaction simulation, can the system generate a voting transaction draft.