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# Introduction
Agentic AI system Can break down complex tasks, use tools and make decisions across multiple steps to achieve goals. Unlike simple chatbots that answer single questions, agents plan, execute and adapt their approach based on results. This capability opens up possibilities for automation and problem solving that were not possible with earlier AI systems.
Building effective agents requires understanding how to give agency to AI systems while maintaining control and reliability. Here are seven steps to developing agentic AI.
# Step 1: Understanding the Core Agent Loop
Each agent follows a basic cycle: Observe the current state, reason about what to do about it, take action, and observe the results. This loop continues until the agent completes its task or determines that it cannot continue.
- The observation phase involves understanding what information is available and what the purpose is.
- The reasoning phase is where the Large Language Model (LLM) decides what action to take based on its instructions and the current state.
- The action phase executes that decision, whether calling an API, running code, or searching for information.
- Finally, the agent observes the results and incorporates them into its next reasoning step.
Understanding this loop is fundamental. Each component can fail or produce unexpected results. Let your agent design handle these prospects beautifully. Build your mental model around this cycle before writing code.
You can read 7 Must Know Agentic AI Design Patterns To review agent design patterns.
# Step 2: Define clear task boundaries and goals
Agents need well-defined goals. Ambiguous goals lead to confused behavior where the agent takes unrelated actions or never recognizes when it is over. Your task definition should clarify what success looks like and what constraints apply.
For a customer service agent, success can be solving a customer’s problem or raising a human being in the right way. Constraints may include never making promises to refund more than a certain amount. These constraints prevent the agent from taking inappropriate actions in pursuit of its goal.
Write clear objective criteria that the agent can check. Instead of “user support,” answer the user’s question using the knowledge base, or notify them that their question requires human assistance. “Concrete goals enable concrete evaluation.
# Step 3: Choosing the Right Tools for Your Agent
Tools are functions that your agent can call to interact with the environment. These may include searching databases, calling APIs, executing code, reading files, or sending messages. The tools you provide define your agent’s capabilities.
Start with a minimal toolset. Each tool adds complexity and potential failure modes. If your agent needs to retrieve information, give them a search tool. If he needs to do calculations, provide a calculator or code execution tool. If he needs to take actions, provide specific tasks for those actions.
Clearly document each tool in the agent’s instructions. Include the purpose of the tool, required parameters, and what output to expect. A good tool description helps the agent choose the right tool for each situation. Poor specifications lead to tool misuse and errors.
Implement proper error handling in your tools. When a device fails, return informative error messages that help the agent understand what went wrong and possibly try a different approach.
Read on What are agent workflows? Samples, use cases, examples and more To understand how to extend LLM with tools, memory, and retrieval for building agents and workflows. If you want to learn by building, pass Agent AI Hands-on in Python: A Video Tutorial.
# Step 4: Designing effective cues and instructions
yours Agent’s system prompt Does it have a manual? This prompt describes the agent’s purpose, available tools, how to reason through problems, and how to format its responses. The prompt quality directly affects the reliability of the agent.
Structure your prompt with clear sections: agent roles and goals, available tools and how to use them, reasoning strategies, output format requirements, and constraints or rules. Use examples to show the agent how to handle common scenarios.
Include clear reasoning instructions. Ask the agent to think step by step, verify information before acting, acknowledge uncertainty, and ask for clarification when needed. These meta-cognitive instructions improve decision quality.
For complex tasks, teach the agent to plan before executing. A planning step where the agent outlines his approach often leads to more coordinated execution than jumping directly into action.
# Step 5: Implementing robust state and memory management
Agents operate at multiple points, and shape the context as they operate. Managing both state and memory Effectively necessary. The agent needs access to the conversation history, the results of previous actions, and any intermediate data collected.
Design your state representation carefully. What information does the agent need to track? For a research agent, this may include questions already tested, sources found and information extracted. For the scheduling agent, it may include available time slots, participant preferences, and constraints.
Consider token limitations. Long conversations can exceed context, forcing you to implement memory management strategies.
- A summary compresses old material into a concise summary while preserving key facts.
- Sliding windows keep recent exchanges in full detail while older context is condensed or collapsed.
- Selective retention identifies and protects important information — such as user preferences, work goals, or important decisions — while removing less relevant details.
For complex agents, implement both short-term and long-term memory. Short-term memory maintains the immediate context necessary for the current task. Long-term memory stores information that should persist across sessions, such as user preferences, learned patterns, or reference data. Store long-term memory in a database or vector store that the agent can query when needed.
Make state changes visible to the agent. When an action modifies the state, clearly show the agent what changed. This helps in understanding the impact of his actions and planning the next steps accordingly. Format state updates consistently so that the agent can reliably analyze and reason about them.
You can read AI agent memory: what, why and how it works By the MEM0 team for a detailed review of memory in AI agents.
# Step 6: Build in guards and safeguards
Agentic systems need constraints to prevent harmful or unintended behavior. These protectors work on several levels: What tools the agent can access, what actions those tools can take, and what decisions the agent is allowed to make autonomously..
Implement action verification for high-stakes operations. Mandate human approval before an agent sends an email, makes a purchase, or deletes data. This Human loop-loop approach Prevents costly mistakes while providing automation for routine tasks.
Set clear limits on the agent’s behavior. A maximum number of until loops prevents an infinite loop. Maximum cost budgets prevent great external systems. Rate limits prevent large external systems.
Monitor failure modes. Interrupt if the agent repeatedly attempts the same unsuccessful action. If the tool starts spewing calls that don’t exist, stop it. If it goes away, redirect it. Implement circuit breakers that stop execution when something goes wrong.
Log all agent actions and decisions. This audit trail is invaluable for debugging and understanding how your agent behaves in production. When something goes wrong, the logs show you exactly what the agent was thinking and doing.
You can check Advanced defenders for AI agents James Briggs tutorial to learn more.
# Step 7: Test, evaluate and continuously improve
Agent behavior is difficult to predict. You can’t anticipate every scenario, so rigorous testing is essential. Create test cases covering common scenarios, edge cases and failure modes.
Evaluate both task completion and behavioral quality. Did the agent accomplish the goal? Did it do that effectively? Did he follow directions and obstacles? Did it handle errors properly? All these dimensions matter.
Test with Adverbial Input:
- What happens if the tools return unexpected data?
- What if the user provides conflicting instructions?
- What if external APIs are down?
Strong agents handle them gracefully instead of breaking them. Measure performance where possible. Track success rates, number of steps to complete, tool usage patterns, and cost per task. These metrics help you identify improvements and catch regressors.
User feedback is important. Real-world use uncovers problems that are often overlooked. When users report problems, trace the agent’s decision-making process to understand what went wrong. Was it an immediate problem? A device problem? A failure of reasoning? Use these insights to improve your agent.
If you are interested in learning more, you can go through it Evaluating AI agents Course by deeplearning.ai.
# The result
Agentic AI is an exciting area that has gained significant interest and adoption. Thus, there will always be new frameworks and better design patterns.
Remaining current with progress is essential. But fundamentals such as clear goals, appropriate tools, good notation, robust state and memory management, appropriate guards, and continuous evaluation remain unchanged. So pay attention to them.
Once you have these fundamentals down, you’ll be able to build agents that reliably solve real problems. The difference between an impressive demo and a production-ready agent lies in thoughtful design, careful constraint management, and rigorous testing and evaluation. Keep building! Also, if you’re looking to educate yourself on Agent AI, check out Agentic AI: A Self-Study Roadmap For a structured learning path.
# Useful learning resources
Bala Priya c is a developer and technical writer from India. She loves working at the intersection of mathematics, programming, data science, and content creation. His areas of interest and expertise include devops, data science, and natural language processing. She enjoys reading, writing, coding and coffee! Currently, she is working on learning and sharing her knowledge with the developer community by authoring tutorials, how-to guides, opinion pieces and more. Bala also engages resource reviews and coding lessons.