AWS Certified Generative AI Developer - Professional: Multi-step tool calls + tracing

Day 20, multi-step tool calls and

tracing. Day 20 is where AWS stops

testing whether you can design agents

and starts testing whether you can debug

them in the real world because a system

you can't see inside is a system you

can't trust, fix, or ship.

Imagine this. A country runs an AI power

grid operator agent. Its job is to

detect outages, identify affected

regions, check safety rules, dispatch

repair crews, and notify authorities.

This is not one API call. It's a chain

of decisions and actions. One night,

something goes wrong. The agent

dispatches the wrong crew. It repeats

the same API call. It takes 45 seconds

to respond. Management asks one

question. Why did the agent do that? And

suddenly, you realize something

terrifying. Without tracing, you have no

idea.

Let's make this clear. Multi-step tool

calls mean the agent is calling multiple

tools in sequence where each result

changes what happens next. This is not a

static workflow. This is not a batch

job. This is not parallel execution. It

is dynamic, stateful, and

decision-driven. And that's exactly why

it breaks in surprising ways. Here's a

simple example. A user says, "Restore

power to the affected suburbs." The

agent does not solve this in one step.

It calls a tool to detect the outage,

then a tool to find affected regions,

then a tool to check safety rules, then

a tool to dispatch a repair crew, then a

tool to notify authorities. Each step

depends on the previous one. If step two

is wrong, everything after it is wrong.

This is why multi-step agents are

powerful and dangerous at the same time.

Now, lock this loop into your head. A

goal comes in. The planner decides the

next step. A tool is called. The tool

returns structured output. The agent

observes the result. Memory is updated.

Then the planner decides again. Agents

loop. They don't run once and stop. If

you understand that loop, you understand

why tracing is mandatory. Here's the

brutal truth AWS wants you to

internalize.

If something goes wrong, you must be

able to answer which tool was called,

with what input in what order, how long

did each step take, which step failed,

did the agent retry, did it loop? If you

can't answer those questions, the system

is unownable. AWS exams quietly reward

engineers who think this way. So, what

does tracing mean in AWS terms? Tracing

means endto-end visibility across the

entire agent flow. You can see LLM

decisions, tool calls, Lambda

executions, retries, timeouts, and

failures. Tracing lets you reconstruct

exactly what the agent did and why it

did it. In AWS, this is built from three

pillars. Cloudatch logs capture tool

inputs, outputs, and errors. AWS X-Ray

gives you distributed traces showing

latency and dependencies across steps,

and structured logging ties everything

together using trace IDs and correlation

IDs. These are not optional in

production and they are absolutely

exam-friendly answers. Here's what AWS

expects you to log for each tool call.

You log a trace ID so steps can be

linked. You log the step number. You log

the tool name. You log the input

parameters. You log the output status.

You log latency in milliseconds. And you

log error codes if anything fails. With

that, you can debug, replay, audit, and

even analyze cost. Without it, you are

guessing. Let's talk about the failures

AWS loves to test. First, infinite

loops. The symptom is simple. The same

tool keeps getting called. The cause is

almost always the same. Memory isn't

updated or the tool result is ignored.

The fix is not a better prompt. The fix

is updating memory after each step and

enforcing a maximum number of steps.

Second, slow responses. The agent takes

too long. This usually comes from too

many steps, slow tools, or no streaming.

The fix is to stream partial responses,

cache tool results, and reduce

unnecessary steps. AWS wants you to

optimize architecture, not just models.

Third, wrong actions. The agent executes

the wrong tool. This happens when tool

schemas are unclear, tool names are

ambiguous, or the planner lacks facts.

The fix is improving tool descriptions,

adding rag before planning, and

validating tool outputs. Again, not a

bigger model. Here's something subtle

AWS loves. Tracing is not just for

debugging. Tracing also shows you which

steps cost the most, which tools

dominate latency, and where token usage

spikes. If the exam asks, "How do you

identify bottlenecks in an agent?" The

answer is not guessing. The answer is

tracing and logs. Now, watch for the

traps. Do not use a bigger model to

debug behavior. Do not add more prompts

to understand failures. Do not rely on

chat history as tracing. None of those

give you observability. Only structured

logs and distributed tracing do. Here is

the one sentence to memorize. Multi-step

agents must be traceable or they are

unfixable. Say that once and day 26.

Final self test. An agent calls several

tools dynamically and sometimes fails in

production. How do you determine where

it failed? Add structured logging and

distributed tracing using Cloudatch and

X-Ray. That's day 20 mastered.