An AI agent is a language model connected to a reasoning loop and external tools. Instead of answering a single message and stopping, it receives a goal, decides on a first action, executes it, reads the result, and keeps going until the task is done. That loop — plus the ability to call APIs, run code, or write to databases — is what separates an agent from a chatbot.
Key takeaways
Three things define an agent: a goal, tools to act on the world, and a loop that keeps reasoning until the task is complete.
Agents exist on a spectrum from a chatbot with a system prompt to a fully autonomous system that executes multi-step workflows without human input.
Most business use cases only need a Tier 2 tool-using agent — one that drafts recommendations for human review before taking action.
The orchestration layer, not Claude's reasoning, is where most agent failures actually occur; invest in error handling before prompt engineering.
If your workflow is "user asks question → AI answers," you do not need an agent — you need a better system prompt.
Key terms
ReAct loop
A reasoning pattern where an AI model alternates between deciding on an action (Reason) and executing it (Act), looping until a goal is complete.
Tool use
The ability of a language model to call external functions — such as APIs, search engines, or databases — rather than only generating text.
Orchestration layer
The code or framework that manages an agent's loop: triggering steps, passing results between them, handling errors, and deciding when to stop.
System prompt
A set of instructions given to an AI model at the start of a session that defines its role, constraints, available tools, and behavior.
Human-in-the-loop
A design pattern where a human reviews and approves an agent's recommendations before any real-world action is taken, reducing the risk of automated mistakes.
---
What is an AI agent, exactly?
An AI agent is any system where a language model doesn't just respond to a single message — it decides what to do next based on a goal and takes action accordingly. [1] The confusion around the term is real and valid: "agent" spans an enormous range of behavior, from a Claude conversation with a custom system prompt to a fully autonomous system that browses the web, writes code, executes it, checks the output, and loops back to fix errors — all without a human in the loop.
This question comes up constantly in the r/ClaudeAI community, and it's one of the most reasonable sources of confusion in AI right now. Both the simple prompted assistant and the fully autonomous workflow technically qualify as "agents." The gap between them is enormous.
Three components define a genuine agent:
A goal or task — something the agent is trying to accomplish, not just answer.
Tools or actions — the ability to do something beyond generating text: search the web, call an API, write to a database, run code.
A loop — the model evaluates results, decides what to do next, and continues until the task is complete or hits a defined stopping point.
Remove any one of these and you have a chatbot or a single API call, not an agent. That distinction matters when you're deciding whether to build an agent or just write a better prompt.
Key Insight: The word "agent" describes a spectrum of AI behavior — from a chatbot with a job description to a fully autonomous system that takes real-world actions. Knowing where on that spectrum your use case actually lives saves you months of over-engineering.
---
How does the reasoning loop actually work?
A standard Claude conversation is stateless: you send a message, Claude sends back text, done. [2] An agent works differently: you give it a goal, Claude decides on a first action, executes it, reads the result, decides on the next action, and keeps going.
This is often called a ReAct loop (Reason + Act) — the engine under every production agent. In Buddy, the AI Google Ads agent I built on top of Claude, the loop looks like this:
Analyze performance metrics and identify anomalies.
Draft recommendations with specific changes.
Optionally push approved changes back to the account.
Each step informs the next. Claude isn't answering a question — it's navigating a workflow. That is the functional difference between a chatbot and an agent.
---
What are the different types of agents?
Rather than debating abstract definitions, it's more useful to think about agents in three practical tiers based on how much autonomy they have.
Tier
What It Does
Human Involvement
Example
Tier 1: Prompted Assistant
Responds to queries with a specialized persona or context
Human initiates every interaction
Claude with a system prompt: "You are an SEO copywriter for e-commerce brands"
Tier 2: Tool-Using Agent
Takes actions, calls APIs, reads & writes data
Human reviews output before action is finalized
Claude that pulls analytics data, writes a report, and posts it to Slack
Tier 3: Autonomous Agent
Executes multi-step workflows end-to-end with minimal oversight
Human sets the goal; agent handles execution
An agent that monitors ad spend, detects overpacing, and pauses campaigns automatically
Most marketers and business owners who say they want "an AI agent" actually need a Tier 2 tool-using agent — not full autonomy. Tier 3 is powerful but requires bulletproof guardrails. I've seen autonomous agents do genuinely impressive things, and I've seen them go sideways fast when a tool returns unexpected data and the model confidently takes the wrong next step.
Best Practice: Start at Tier 2. Build the agent so it drafts and presents recommendations, then add automation only after you've reviewed enough outputs to trust its judgment. For anything touching budget or live systems, human-in-the-loop isn't a weakness — it's responsible design.
What are the technical building blocks of an agent?
If you want to build or evaluate an agent — not just use one — four components determine how it behaves.
1. The system prompt
Every agent starts with a system prompt that defines its role, constraints, available tools, and error-handling instructions. [4] A production system prompt might run 500–2,000 words. Don't underestimate this — it is the foundation everything else rests on.
2. Tools
Tools are functions the model can call to interact with the outside world. Claude's tool-use API lets you define tools with names, descriptions, and parameter schemas; the model reads these definitions and decides when and how to invoke them. [2] Common tools in marketing and advertising agents include:
Google Ads API or Meta Marketing API — pull performance data, push changes [3]
Web search — gather competitive intelligence or current pricing
Code execution — run calculations, process CSVs, generate charts
Email or Slack webhooks — deliver outputs to the right people
Common Mistake: Vague tool descriptions. Claude decides when to use a tool based on how you describe it. "Gets data" is useless. Write tool descriptions as if you're explaining them to a smart but brand-new junior analyst — specific, with clear examples of when to use and when not to.
3. Memory
LLMs don't have persistent memory by default — each conversation starts fresh. Agents handle this through three patterns:
In-context memory: Stuffing relevant history into the prompt window. Works up to a point, then gets expensive.
External memory: A database the agent reads from and writes to — previous decisions, account history, user preferences.
Summarization: Periodically compressing older context into shorter summaries before appending new data.
For Buddy, I use a lightweight external store that holds the last 30 days of account performance snapshots and any action history. When the agent starts a new session, it retrieves relevant context before doing anything else.
4. The orchestration layer
Something has to manage the loop — trigger the agent, pass results between steps, handle errors, and know when to stop. You can build this in Python, use a framework, or for simpler cases, a while-loop that keeps calling the Claude API until the model returns a stop signal.
I prefer lightweight custom orchestration for production agents because frameworks add abstraction that makes debugging harder when things go wrong.
Key Insight: The orchestration layer is where most agent failures actually live — not in Claude's reasoning. Unexpected API responses, tool timeouts, and malformed outputs break the loop. Invest in robust error handling here before you invest in prompt engineering.
For teams exploring protocol-based tool connections, the Model Context Protocol (MCP) is worth understanding — it standardizes how agents connect to external data sources and tools. [5]
---
What are the most common mistakes when building AI agents?
Common Mistake: Treating "agent" as a product category rather than an architecture. Many tools now market themselves as "AI agents" when they're really chatbots with a few API integrations. Before building or buying, ask: does this system actually reason across multiple steps, or does it just route a single input to a single output?
Here are the mistakes I see most often in practice:
Too much autonomy too fast. Jumping straight to Tier 3 without validating the agent's judgment at Tier 2 is how you end up with automated systems making expensive mistakes. I've seen agents pause perfectly healthy campaigns because an anomaly detection threshold was misconfigured.
No stop conditions. An agent without clear stopping criteria will keep looping. Define explicit success conditions ("task complete when a report has been posted to Slack") and failure conditions ("stop and alert a human if more than 3 tool calls return errors").
Ignoring latency and cost. Multi-step agents burn through tokens fast. A 10-step agentic workflow can cost 10x–50x more than a single well-crafted prompt. Profile your agents before deploying at scale.
No logging. If you can't replay what the agent did and why, you can't debug or improve it. Log every tool call, every response, and every decision point.
Common Mistake: Building an agent for a problem a good prompt already solves. If your workflow is "user asks question → AI answers" with no multiple steps, no external data, and no ongoing tasks, you don't need an agent. You need a better system prompt. Agents add complexity — only add that complexity when you need the capabilities that come with it.
What does an agent look like in a real marketing workflow?
Agents shine in marketing when a workflow is too complex or repetitive for a human to do well at scale, but too judgment-intensive for a simple rule-based automation. Here are three patterns that work in practice.
Paid media audit agent
Goal: Review an account weekly and surface the top optimization opportunities. The agent pulls data from the Ads API, analyzes it against benchmarks, drafts actionable recommendations with specific bid and budget changes, and posts the summary to Slack. A human reviews and approves before anything changes. This is a clean Tier 2 use case and is exactly what Buddy does.
Content brief generator
Goal: Given a target keyword, produce a complete SEO content brief. The agent searches current SERPs for top-ranking content, extracts common headings and topics, checks the client's existing content for gaps, and produces a structured brief. No human in the loop until the brief is delivered — Tier 2 with light autonomy because outputs are informational, not operational.
Budget pacing monitor
Goal: Alert when daily spend is tracking more than 15% over or under target. The agent runs on a schedule, calculates projected monthly spend based on current daily rate, and either fires an alert or — in Tier 3 mode — adjusts daily budgets directly. This is where Tier 3 autonomy is genuinely valuable; humans don't need to approve a budget cap adjustment at 2am. But it requires tight guardrails: maximum adjustment under 20% of current budget, never adjust more than once per 6-hour window.
Best Practice: For any agent that touches live systems, implement a "dry run" mode that logs what the agent would have done without actually doing it. Run in dry-run mode for 2–4 weeks before enabling live actions. The delta between what you expected the agent to do and what it actually recommends is almost always illuminating.
Define your workflow before you define your agent. Map out the task step by step — what data does it need, what decisions does it make, what outputs does it produce? If you can't draw this on a whiteboard, you're not ready to build it.
Start with Claude's tool-use API. The Anthropic documentation on tool use is genuinely good. [2] Build a simple two-tool agent — one to fetch data, one to format output — and run it manually. Understand the loop before you automate it.
Default to Tier 2. Build every agent with a human-review step first. Earn the right to remove that step through demonstrated accuracy over at least 50–100 real task cycles.
Log everything from day one. Every tool call, every response, every decision. You'll use this data to debug, improve prompts, and justify the agent's performance to stakeholders.
Check out Buddy if you're in paid media. It's open-source, built on Claude, and shows exactly how a production Tier 2/3 agent handles real Google Ads workflows — including orchestration, tool definitions, and memory patterns. Getting started with Buddy walks through the setup.
---
Frequently asked questions
Is an AI agent the same as a chatbot?
No. A chatbot responds to a single message and stops. An agent receives a goal, takes an action, reads the result, decides what to do next, and keeps going until the task is complete. The reasoning loop and the ability to call external tools are what distinguish an agent from a chatbot — even a very sophisticated one.
Do I need to know how to code to build an agent?
For a production agent with real tools and a proper orchestration layer, yes — at least enough Python to call an API and handle errors. That said, lightweight hosted options are emerging that reduce the coding requirement. If you're evaluating rather than building, understanding the architecture (system prompt, tools, memory, orchestration loop) matters more than being able to write it yourself.
How much does running an AI agent cost compared to a regular Claude conversation?
Significantly more. A multi-step agentic workflow can cost 10x–50x more in API tokens than a single well-crafted prompt, because every tool call and its result gets appended to the context, and the model reasons over that growing context repeatedly. Profile your agent on representative tasks before deploying at scale to avoid bill shock.
What is the Model Context Protocol (MCP) and how does it relate to agents?
MCP is an open standard that defines how AI models connect to external tools, data sources, and services — essentially a common interface so agents don't need one-off integrations for every system they touch. [5] It's particularly useful when you want an agent to work across multiple tools without rebuilding the connection layer each time. Buddy supports MCP, which you can explore at the MCP integration docs.
When should I use an agent versus just a better prompt?
Use a prompt when your workflow is a single round-trip: user input in, AI output out. Use an agent when the task requires multiple steps, external data, or decisions that depend on previous results. The test is simple: can you complete this task in one AI call? If yes, prompt. If the task has branching logic, requires live data, or produces outputs that feed back into further reasoning, build an agent.
Are there risks to giving an agent access to live systems like Google Ads?
Yes, and they're worth taking seriously. An agent with write access to a live ad account can make expensive mistakes if a tool returns unexpected data and the model takes confident action on it. The mitigations are: start with read-only access, validate output accuracy over 50–100 real task cycles before enabling writes, implement a dry-run mode, set strict guardrails on the magnitude of any single change, and log every action so you can audit what happened.
AI Disclosure: This article was generated with AI assistance based on a community discussion on Reddit r/ClaudeAI. Expert analysis and practitioner perspective by John Williams, Founder, AHMEEGO · Google Ads Practitioner with $350M+ in managed Google Ads spend. AI was used to draft and structure the content; all strategic recommendations reflect real campaign experience.
