How Does AWS AgentCore Actually Think, Plan, and Act? Inside the Brain of a Bedrock Agent

Let's imagine building an AI-powered Customer Support Bot named Sparky. Sparky answers customer queries, fetches order details, and even updates tickets in JIRA. Here's how AgentCore helps Sparky do all that — step by step:

🏁 Step 1: Sparky Wakes Up — The Runtime

When Sparky gets a new customer request, it's like an athlete waking up to race. The Runtime gives Sparky a serverless brain — automatically providing the CPU and memory it needs, just for the duration of that conversation.

🧭 Step 2: Finding Tools — The Gateway

Now, Sparky needs to check the order status. Instead of hardcoding APIs, Gateway lets it "discover" tools like OrderAPI or TicketAPI on the fly. It's like giving Sparky a magic toolbox where it can pick the right wrench for the job.

🛡 Step 3: Access Permission — The Identity

Before touching customer data, Sparky shows its AWS Identity Badge — verifying it has permission to access those APIs. This ensures no unauthorized tool is ever used.

Think of it as a bouncer checking ID before letting someone enter the club.

🧠 Step 4: Remembering the Context — The Memory

Sparky remembers that "Paresh asked about refund policy last week." That's short-term memory. It also recalls that "Paresh usually prefers email over chat." That's long-term memory.

Just like humans — short-term memory helps in the moment, long-term memory helps personalize the future.

🌐 Step 5: Browsing and Learning — The Browser

If Sparky needs to look up new product policies, it uses AgentCore Browser. It's like giving Sparky its own private, safe Chrome window in the cloud.

🧪 Step 6: Thinking Deeper — Code Interpreter

If Sparky needs to calculate something — like refund amount after discount — it uses the Code Interpreter. That's like a mini data scientist living inside Sparky, writing and executing code securely in a sandbox.

# Sparky calculates refund amount
order_total = 299.99
discount_applied = 0.15
tax_rate = 0.08

# Calculate refund
discounted_price = order_total * (1 - discount_applied)
refund_amount = discounted_price * (1 + tax_rate)

print(f"Refund amount: ${refund_amount:.2f}")
# Output: Refund amount: $277.91

🔍 Step 7: Keeping Track — Observability

Finally, everything Sparky does is recorded for debugging and performance tracking through Observability. If something goes wrong, developers can see exactly what Sparky was thinking.