How to build AI Agent

 7 steps to build production-ready AI agents.

Most teams skip steps 6 and 7

Here's the complete process:

1. 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝗮 𝗚𝗼𝗮𝗹

* Problem clearly defined with measurable goals
* Choose the right workflow design pattern
* Identify the right points for HITL (Human-in-the-Loop)
* Define the agent's constraints

No clear goal = agent that does everything poorly.

2. 𝗣𝗶𝗰𝗸 𝘁𝗵𝗲 𝗥𝗶𝗴𝗵𝘁 𝗠𝗼𝗱𝗲𝗹

Match model to use case:

* LRM: For complex reasoning Use cases like coding
* LLM: Best for average token efficient use cases
* SLM: Best for query routing and rewriting

Wrong model = wasted cost or poor performance.

3. 𝗖𝗵𝗼𝗼𝘀𝗲 𝘁𝗵𝗲 𝗥𝗶𝗴𝗵𝘁 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸

Simple Workflows:

* Gumloop
* Langflow
* Langchain
* Google ADK

Production:

* Dify
* n8N
* Smol agents
* CrewAI
* Llamaindex
* OpenAI Agent SDK

Framework determines scalability.

4. 𝗖𝗼𝗻𝗻𝗲𝗰𝘁 𝗧𝗼𝗼𝗹𝘀

* Connect with MCP
* Using Agent as tools
* Functional calling
* File System Access

No tool integration = limited agent capability.

5. 𝗗𝗶𝘃𝗶𝗱𝗲 𝗠𝗲𝗺𝗼𝗿𝘆

Cache Memory:
Most used for current conversations

Episodic Memory:
Recall specific past experiences/events

File System Memory:
Persistent storage of structured data/documents

Poor memory design = agent can't learn or improve.

6. 𝗠𝗮𝗻𝗮𝗴𝗲 𝗖𝗼𝗻𝘁𝗲𝘅𝘁

* Compress old context through summarization
* Monitor context effectiveness with metrics
* Add context intelligently based on current need

Context mismanagement = degraded performance over time.

7. 𝗧𝗲𝘀𝘁 𝗮𝗻𝗱 𝗘𝘃𝗮𝗹𝘀

* Unit tests for specific functions and workflows
* Edge case discovery for core processes
* Cost per successful task performed by agent

No testing = production disasters.

Most teams stop at step 5.

They build the agent, connect tools, set up memory.

Then deploy.

They skip context management. They skip testing.

Then production happens.

Agent performance degrades. Costs spiral. Edge cases break everything.

Steps 1-5 build the agent.

Steps 6-7 make it production-ready.

Skip them, and you're deploying a prototype, not a product.