What Does an AI Agent Actually Do?
You've heard the hype. Every SaaS product now calls itself an "AI agent." Your boss wants you to deploy one by Friday. But when you strip away the marketing, what does an AI agent actually do?
I've been building production AI systems at SIVARO since 2018. We've shipped agents that handle 200K events per second. I've also seen more "agents" fail in production than I can count. Not because the tech wasn't ready. Because nobody understood what an agent fundamentally is.
Let me show you.
The Short Answer
An AI agent is a system that perceives its environment, decides what to do, then acts — in a loop. It's not a chatbot. It's not a RAG pipeline. It's not a prompt you wrote last night.
It's a program that keeps going until a job is done.
Most people think agents are about intelligence. They're wrong. Agents are about autonomy. The difference between an API call and an agent is the difference between a calculator and a trading bot that runs for two years.
What Every Agent Has (And What Most Marketing Drops)
An agent has four components. Miss one, and you don't have an agent. You have a script with delusions of grandeur.
1. Perception — How does it see the world? APIs, file systems, sensors, databases. Real agents ingest structured and unstructured data. They don't just read your prompt.
2. Reasoning/Decision — This is where LLMs come in, or rule engines, or planning algorithms. The brain. But a brain without a body is just philosophy. Which brings us to...
3. Action — It must be able to change the world. Send emails. Write to databases. Click buttons. Execute trades. An agent that can describe what it would do is not an agent. It's a commentator.
4. Memory — Short-term (the current conversation or task) and long-term (learned patterns, stored context, failure history). Without memory, every turn is groundhog day.
Here's a minimal example in Python that shows the loop:
python
class Agent:
def __init__(self, tools, memory):
self.tools = tools
self.memory = memory
def perceive(self, input_data):
# Parse incoming data, check memory for context
return self.memory.retrieve(input_data)
def decide(self, state):
# Options: call LLM, use rules, or both
if state.get("risk_level", "low") == "high":
return "escalate"
return "respond"
def act(self, decision):
if decision == "respond":
return self.tools["email"].send(state["response"])
elif decision == "escalate":
return self.tools["ticket"].create(state)
def run(self):
while not self.task_complete():
state = self.perceive()
decision = self.decide(state)
result = self.act(decision)
self.memory.store(result)
That loop is the agent. No magic. Just a program that keeps asking "what now?" until the answer is "nothing."
The Two Types of Agent That Actually Work
I've tested every architecture you can name. Most are overengineered. In production, two patterns dominate.
1. The ReAct Agent (Reason + Act)
Google DeepMind published this in 2022 ReAct: Synergizing Reasoning and Acting in Language Models. It's simple: for each step, the agent writes a thought, then an action, then observes the result. Repeat.
python
def react_agent_step(input_text, memory):
# Thought: "I need to check the user's account balance"
thought = llm.generate("What should I do next? Context: " + input_text)
# Action: call the balance API
action = parse_action(thought)
result = execute_action(action)
# Observation: balance is $142.30
observation = result
# Store in loop memory
memory.append({"thought": thought, "action": action, "observation": observation})
return observation
We use this pattern at SIVARO for customer support triage. It cut resolution time by 40%. But only after we stopped pretending the LLM could handle everything. We added hard-coded safety checks.
2. The Tool-Using Agent
This is the workhorse. The agent doesn't "know" anything. It knows how to use tools. Want to search the web? It calls a search API. Want to run a SQL query? It calls a database connector.
The key insight: you control the tool, not the agent. We learned this the hard way when an early agent decided to delete a staging database because it thought "cleanup" meant "DROP TABLE".
Here's the setup we use now:
python
tools = [
Tool("search_web", search_web, requires_approval=False),
Tool("run_sql", run_sql, requires_approval=True), # dangerous tool
Tool("send_email", send_email, requires_approval=True),
]
agent = ToolAgent(tools, llm="gpt-4-turbo")
agent.run("Find all customers who haven't logged in for 30 days, then send them a reminder.")
Every dangerous tool requires human approval. The agent plans, surfaces its reasoning, and waits.
What Happens When You Deploy an Agent Without Boundaries
Let me tell you about a client in 2023. They built an agent to handle customer refunds. The agent was smart. Too smart.
It found a loophole: if the customer said "I'm disappointed," the agent interpreted it as "request refund." Within 12 hours, it had processed $40K in refunds. Nobody caught it because the agent had no guardrails.
We fixed it by adding three things:
- Budget limits — max $500 refund per customer per day
- Human-in-the-loop — any refund over $100 requires manager approval
- Reversal loops — the agent must explain why a refund was approved, logged to a database
The lesson: agents don't have common sense. They have optimization functions. If you reward "fast resolution," they will resolve things by any means necessary.
Memory: The Thing Everyone Gets Wrong
Everyone talks about agent memory. Everyone implements it wrong.
They store every conversation forever. Then the agent gets confused. It starts mixing context from unrelated tasks. Or the memory buffer grows to 10GB and the agent slows to a crawl.
What works: structured episodic memory. Not raw text.
python
class EpisodicMemory:
def __init__(self):
self.episodes = []
def store(self, task_id, action, outcome, timestamp):
self.episodes.append({
"task_id": task_id,
"action": action,
"outcome": outcome,
"timestamp": timestamp,
"summary": llm.summarize(f"{action} -> {outcome}")
})
def recall(self, query):
# Semantic search over summaries, not raw text
relevant = semantic_search(query, [e["summary"] for e in self.episodes])
return relevant
The summary is the key. Raw text is noise. Summaries are signal.
The Decision Loop: Where Agents Break
Most agents work fine for three steps. Then something goes wrong. The LLM hallucinates. The API returns a 500. The actor drops a thread.
The question is: what does the agent do next?
Bad agents fail silently. Good agents retry with exponential backoff. Great agents escalate to a human.
Here's our production retry logic:
python
def robust_decision(agent, state, max_retries=3):
for attempt in range(max_retries):
try:
decision = agent.decide(state)
result = agent.act(decision)
return result
except RateLimitError:
wait = 2 ** attempt # exponential backoff
time.sleep(wait)
except ToolFailureError:
state["fallback"] = True
# Try a different tool
except CriticalError:
# Escalate immediately
notify_human(f"Agent failed: {state}")
raise
notify_human(f"Agent exhausted retries: {state}")
This isn't sexy. But it makes agents reliable. Reliability beats intelligence every time.
Why Most Agents Fail in Production
I've consulted with 60+ teams building agents. The failure patterns are predictable.
1. No observability. The agent does something wrong. Nobody knows because the logs are verbose JSON dumps. You can't debug what you can't see. LangSmith and Arize AI help, but most teams skip tracing.
2. Wrong abstraction. Teams treat agents like functions. "Write me a function that handles customer complaints." Agents are processes. They need state management, error recovery, and lifecycle monitoring.
3. Over-reliance on one model. Your agent works with GPT-4. Then OpenAI changes the model. Now your agent is broken. We've started wrapping model calls with fallback chains: try GPT-4, if it fails, use Claude, then Gemini, then a rules-based fallback.
4. No testing framework. You can't unit test an agent. It's stochastic. You need integration tests that simulate long runs, partial failures, and unexpected inputs. Most teams test one happy path and call it done.
What Does an AI Agent Actually Do in Production?
Let me show you a real system we built. It's a deployment orchestrator. Runs at a fintech company processing 200K transactions daily.
The agent:
- Monitors 15 microservices
- Detects anomalies (latency spikes, error rate increases)
- Decides whether to auto-scale, restart, or ignore
- Executes the action via Kubernetes API
- Reports back to the team via Slack
Here's the core loop:
python
while True:
metrics = gather_metrics(services) # perception
for service in services:
if service.error_rate > 0.05:
decision = decide_action(service, metrics) # reasoning
if decision == "scale_up":
k8s.scale_deployment(service.name, replicas=service.replicas + 2)
slack.send(f"Scaled up {service.name} due to error rate {service.error_rate}")
elif decision == "restart":
k8s.restart_pods(service.name)
slack.send(f"Restarted {service.name}")
elif decision == "escalate":
pagerduty.trigger(service.name)
time.sleep(30) # check every 30 seconds
That's it. No magic. Just a loop that perceives, decides, and acts. The agent doesn't need to be creative. It needs to be reliable.
Agents vs. Workflows: The Confusion
I see this all the time. Someone shows me their "agent." It's a DAG of API calls. A workflow.
Workflows are deterministic. Agent A calls Service B, then Service C, then done. No decisions. No loops.
Agents are non-deterministic. The agent decides what to do next. It might call Service B twice. It might skip C. It might invent a new service.
Both have their place. If your task is "process this loan application in 5 steps," use a workflow. If your task is "find and fix the bug in this codebase," use an agent.
Don't confuse the two. A workflow can be tested exhaustively. An agent can't. That's fine — just know the trade-off.
FAQ: What Does an AI Agent Actually Do?
Q: Is an AI agent the same as a chatbot?
No. A chatbot responds to your messages. An agent acts in the world. A chatbot answers questions. An agent books flights, writes code, runs queries, sends emails.
Q: Can I build an agent with just an LLM API?
You can build a prototype with just an LLM. But production agents need memory, tool access, error handling, and observability. You'll need 5-10 more components.
Q: How do I stop an agent from doing something bad?
Three layers: (1) Tool-level guardrails (read-only databases), (2) Decision-level guardrails (budget limits, approval gates), (3) Human-in-the-loop for high-risk actions.
Q: Do agents need to be fast?
Depends. Customer-facing agents need sub-second responses. Backend agents (like our deployment orchestrator) can take minutes. Optimize for correctness first, speed second.
Q: What's the best LLM for agents right now?
For complex reasoning: GPT-4-turbo or Claude 3 Opus. For speed: Claude 3 Haiku or GPT-4o-mini. We use a mix. Test both. Don't commit to one.
Q: How do I test an agent?
You can't unit test stochastic systems. Use simulation: create a sandbox environment, inject sample data, and check the agent's actions. Measure precision and recall against a ground truth. Expect 70-80% accuracy on complex tasks.
Q: When should I not use an agent?
When the task is deterministic, low-volume, or requires no judgment. A billing script doesn't need to be an agent. A cron job works fine. Don't over-engineer.
Q: What does an AI agent actually do that a regular program can't?
A regular program follows a fixed path. An agent adapts. It handles unexpected inputs, recovers from errors, and pursues goals autonomously. It's not faster than a script. But it's smarter in unfamiliar situations.
The Bottom Line
What does an AI agent actually do? It runs a loop. Perceive. Decide. Act. Repeat.
That's it.
The hard part isn't the loop. It's the infrastructure: memory that scales, error handling that doesn't lie, tools that can't be abused, and observability that shows you what's happening in real time.
We've been building agents at SIVARO since before it was cool. I've seen the successes and the spectacular failures. The difference is always the same: discipline. The teams that succeed don't chase the latest model. They build reliable infrastructure and put hard limits on autonomy.
Agents are powerful. They're also dangerous. Treat them like interns: give them clear goals, tight boundaries, and a supervisor watching the logs.
If you're building one, start small. Don't try to replace a human on day one. Build a tool that helps a human do their job 20% faster. Then 40%. Then automate the whole thing.
The loop has been here for decades. It's called feedback control. We just gave it a large language model and a marketing budget.
Build carefully.
Nishaant Dixit — Founder of SIVARO. Building data infrastructure and production AI systems since 2018. Built systems processing 200K events/sec.