Congratulations on completing this course.
You now have deep insight into the fundamentals of building AI agents.
You're ready to apply your skills and pursue new opportunities to continue your learning.
Before you move on, let's review your key takeaways from this course.
You now know that tools help LLMs access external data and support RAG, enabling the use of
the organization's or other specialized databases.
Tools also help process images, audio, and video to enable vision, voice, and multimodal
reasoning, manage long conversations, and connect to APIs to perform real-world actions.
Embedded tool calling improves LLM accuracy and reduces hallucinations by centralizing
tool handling within a dedicated library or framework, replacing error-prone client-side
implementations.
Tools help LLMs select the correct function by briefly describing their purpose, defining
the expected output, and showing the required input and output formats.
Agents select a step-by-step reasoning loop that selects a tool, calls the tool, reviews
the result answer, feeds the result into the process, and repeats the process as needed
until the LLM and tools produce a final response.
The Zero-Shot React agent uses zero-shot reasoning to solve tasks it hasn't seen before and
works best for simple or well-structured problems.
When building an agent in LangChain, choose an LLM that supports tool use and complex
reasoning, uses tools with JSON serializable inputs and outputs, and select an agent strategy
that matches task complexity.
LangChain lets you build flexible agents using create_react_agent to
define how the agent thinks and responds.
You can customize the agent's behavior by passing in your prompt templates and tool
lists to control reasoning style.
You can group tools such as addition, subtraction, multiplication, and division tools into a
custom math toolkit, and the agent can use them to perform calculations, and use the
.invoke method to simulate chat interactions, send messages to the agent, and receive structured
responses.
Next, review what you've learned about manual invocation.
You learned that LLMs specify task parameters and suggest tools, and agents automatically
invoke tools based on direction from the LLM.
You know that manual invocation involves verifying inputs and outputs and adjusting
actions as needed, and that manual invocation provides greater control that can help organizations
enhance safety, manage costs, and deliver more accurate information.
You've learned that you can analyze and visualize data by asking natural language
questions to the Pandas DataFrame agent.
The Pandas DataFrame agent generates Python code that directly interacts with your DataFrame,
filtering, aggregating, and visualizing data based on your natural language prompts.
Your DataFrame refers to the Pandas DataFrame object that you loaded or created and then
passed to the LangChain Pandas agent.
You also know that LCEL pattern structures workflows use the pipe operator for precise
data flow.
You can define prompts using templates that include variables and side {}.
You can link components for sequential execution using runnable sequence, and run multiple
components concurrently on the same input using runnable parallel.
You simplify syntax by replacing runnable sequence with the pipe operator, and LCEL
automatically converts functions and dictionaries into compatible components using type coercion.
No review would be complete without examining best practices when beginning to use agents.
Always start with sandboxed environments, design clear prompts, validate the LLM analysis
with human expertise, and iteratively refine your queries for safe and effective AI-driven
analysis.
AI-powered SQL agents allow a broader range of users to access and interpret data without
needing deep technical skills.
To develop applications that use natural language queries with LangChain, start by creating
a Python virtual environment.
Then install the required libraries, such as LangChain and your LLM, launch the SQL
server, and build a database connector.
Once your environment is ready, you can run natural language queries using LangChain's
SQL agent, which translates those queries into SQL and retrieves results from the database.
With this knowledge, we encourage you to continue learning and apply this course towards an
IBM Professional Certificate.
Depending on your schedule and the number of courses in the program, you can complete
this Professional Certificate in approximately two months.
You'll find the links to the Professional Certificate and several related courses in
the congratulations and next steps reading at the end of this course.
We encourage you to continue practicing your new skills.
We hope your new skills support your current work and enable you to advance your career.
Congratulations on completing this course.
We appreciate your participation in this learning journey and wish you all the best.