AI Agents

AI agents are autonomous systems that go beyond simple question-answering. They can perceive information, reason about it, make decisions, and take actions to accomplish goals.

Unlike a basic chatbot that just responds to queries, an AI agent can:

• Plan: Break down complex goals into steps
• Use tools: Call APIs, search databases, execute code
• Remember: Maintain context across interactions
• Adapt: Adjust behavior based on results
• Act: Take real-world actions like sending emails or updating records

Think of the difference between asking someone a question versus asking them to complete a project. The first is a chatbot; the second is an agent.

AI agents operate through a loop of perception, reasoning, and action:

1. Perception The agent receives input—a user request, a triggered event, or data from its environment. This could be a message like "Schedule a meeting with the sales team next week."

2. Reasoning The agent's LLM "brain" processes the input and determines what to do. This might involve:

• Understanding the intent
• Breaking the task into subtasks
• Deciding which tools to use
• Planning the sequence of actions

3. Action The agent executes actions using available tools:

• Checking calendar availability
• Finding team members' email addresses
• Sending calendar invites
• Confirming completion to the user

4. Observation The agent observes the results of its actions and decides if the goal is achieved or if more steps are needed.

This loop continues until the task is complete or the agent determines it cannot proceed.

Simple reflex agents React to current input without considering history. Example: A spam filter that classifies each email independently.

Model-based agents Maintain an internal model of the world that gets updated with new information. Example: A navigation agent that tracks your location and updates routes based on traffic.

Goal-based agents Work toward specific objectives, planning sequences of actions to achieve them. Example: An AI that plans a trip including flights, hotels, and activities.

Utility-based agents Optimize for a utility function, choosing actions that maximize expected value. Example: A trading agent that balances risk and reward.

Learning agents Improve performance over time through experience. Example: A recommendation agent that learns your preferences from your behavior.

Multi-agent systems Multiple agents working together, each with specialized capabilities. Example: A customer service system with separate agents for billing, technical support, and sales.

AI agents derive their power from the tools they can access:

Information retrieval

• Web search
• Database queries
• Document search (RAG)
• API calls to external services

Communication

• Sending emails
• Posting to Slack/Teams
• SMS notifications
• Creating calendar events

Data manipulation

• Reading and writing files
• Updating CRM records
• Processing spreadsheets
• Generating reports

Code execution

• Running Python scripts
• Executing SQL queries
• Calling webhooks
• Automating workflows

Specialized tools

• Image generation
• Data visualization
• Translation
• Sentiment analysis

The key architectural pattern enabling this is function calling (or tool use), where the LLM can request that specific functions be executed with particular parameters.

Customer support Agents that handle inquiries, look up order status, process returns, and escalate complex issues to humans. They integrate with helpdesk systems, CRMs, and order databases.

Sales assistance Agents that qualify leads, answer product questions, schedule demos, and update Salesforce records. They can follow up with prospects automatically.

Internal operations Agents that help employees with HR questions, IT support, expense reporting, and document retrieval. They reduce the burden on support teams.

Data analysis Agents that query databases, generate reports, create visualizations, and summarize findings in natural language.

Content creation Agents that draft emails, create social posts, write documentation, and maintain consistent brand voice.

Workflow automation Agents that orchestrate multi-step processes across systems—like processing applications, onboarding customers, or managing approvals.

Define the scope Start narrow. What specific task should this agent handle? What tools does it need? What are the boundaries of its authority?

Choose your framework

• LangChain/LangGraph: Popular Python framework with extensive tool integrations
• AutoGen: Microsoft's framework for multi-agent systems
• CrewAI: Focused on role-based agent collaboration
• Chipp: No-code platform for building and deploying agents

Implement tools Create functions the agent can call. Each tool should:

• Have a clear, descriptive name
• Include a detailed description the LLM can understand
• Define required and optional parameters
• Return structured results

Design the prompt Your system prompt defines the agent's personality, capabilities, and constraints. Include:

• Role and purpose
• Available tools and when to use them
• Guidelines for behavior
• Error handling instructions

Add guardrails Implement safety measures:

• Rate limiting
• Action confirmation for sensitive operations
• Scope restrictions
• Human-in-the-loop for high-stakes decisions

Test extensively Agents can behave unpredictably. Test with diverse inputs, edge cases, and adversarial prompts before deploying.

Reliability Agents can make mistakes, especially on complex multi-step tasks. Each step has some failure probability that compounds.

Latency Agent loops take time. Each tool call adds latency. Users may not tolerate long waits for simple requests.

Cost Multiple LLM calls per interaction add up. Tool calls to paid APIs increase costs further.

Security Agents with real-world capabilities pose risks. Prompt injection could trick an agent into unauthorized actions.

Observability Debugging agent behavior is hard. You need logging and tracing to understand what went wrong and why.

User trust Users may be uncomfortable with AI taking autonomous actions, especially for sensitive tasks.

Scope creep Without clear boundaries, agents may attempt tasks beyond their competence, leading to poor results or harmful actions.