Build Your First AI Agent on AWS (in about the time it takes to drink a coffee)

agent = model + tools + a loop + memory

You:   Why did my bill go up in August?
Agent: Your August spend rose ~31% ($1,930 -> $2,530). The main driver is EC2,
       up 39% ($890 -> $1,240) — consistent with three new instances in
       us-east-1 running since Aug 3. S3 and RDS were essentially flat.

# src/cost_tool.py
"""Cost breakdown tool for the AWS Cost Explainer agent.

Mock by default (deterministic, stage-safe). Flip USE_MOCK to False to use the
real AWS Cost Explorer API.
"""

USE_MOCK = True

# Deterministic mock data — a believable month-over-month spike driven by EC2.
_MOCK = {
    "period": {"previous": "July", "current": "August"},
    "currency": "USD",
    "services": [
        {"service": "EC2",        "previous": 890.0, "current": 1240.0},
        {"service": "S3",         "previous": 205.0, "current": 210.0},
        {"service": "RDS",        "previous": 470.0, "current": 480.0},
        {"service": "Lambda",     "previous": 65.0,  "current": 72.0},
        {"service": "CloudWatch", "previous": 40.0,  "current": 43.0},
    ],
    "notes": "Three new EC2 instances launched in us-east-1 on Aug 3.",
}

def get_cost_breakdown() -> dict:
    """Return a month-over-month cost breakdown by service."""
    if USE_MOCK:
        return _MOCK

    # ---- Real AWS Cost Explorer implementation ----
    # Requires IAM permission: ce:GetCostAndUsage
    #
    # import boto3
    # ce = boto3.client("ce")
    # resp = ce.get_cost_and_usage(
    #     TimePeriod={"Start": "2026-07-01", "End": "2026-09-01"},
    #     Granularity="MONTHLY",
    #     Metrics=["UnblendedCost"],
    #     GroupBy=[{"Type": "DIMENSION", "Key": "SERVICE"}],
    # )
    # # reshape resp["ResultsByTime"] into {service, previous, current} rows
    # return reshaped
    raise NotImplementedError("Set USE_MOCK=True or implement the Cost Explorer call above.")

# src/agent.py
"""AWS Cost Explainer — a minimal Strands agent. model + tool + loop."""

from strands import Agent, tool
from cost_tool import get_cost_breakdown as _get_cost_breakdown

@tool
def get_cost_breakdown() -> dict:
    """Fetch a month-over-month AWS cost breakdown by service.

    Use this whenever the user asks about their AWS bill, cost changes,
    spikes, or which service is driving spend.
    """
    return _get_cost_breakdown()

SYSTEM_PROMPT = (
    "You are the AWS Cost Explainer. When a user asks about their AWS bill, "
    "call get_cost_breakdown, compute the month-over-month change overall and "
    "per service, and explain the top drivers in plain, friendly language. "
    "Lead with the headline number, then name the single biggest driver and the "
    "likely cause. Keep it under 5 sentences. Do not invent numbers not in the data."
)

def build_agent() -> Agent:
    # Model defaults to Amazon Bedrock via your AWS credentials.
    return Agent(system_prompt=SYSTEM_PROMPT, tools=[get_cost_breakdown])

def main():
    agent = build_agent()
    print("AWS Cost Explainer agent. Ask about your bill (Ctrl+C to quit).\n")
    while True:
        q = input("You: ").strip()
        if q:
            print(f"\nAgent: {agent(q)}\n")

if __name__ == "__main__":
    main()

aws-cost-explainer-agent/
├── .kiro/specs/cost-explainer/   # the Kiro spec — the source of truth
│   ├── requirements.md
│   ├── design.md
│   └── tasks.md
├── src/
│   ├── agent.py                  # the Strands agent (above)
│   └── cost_tool.py              # the tool (mock by default)
├── requirements.txt
└── README.md

# 1. clone, then:
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt      # strands-agents, boto3

# 2. make sure you have AWS credentials with Bedrock access
aws configure                        # and enable model access in the Bedrock console

# 3. run it (mock data by default)
python src/agent.py
# then ask: Why did my bill go up in August?