How AI Agents Actually Work: A Technical (But Accessible) Guide

We’ve been talking about Agentic AI. Now let’s deep dive into how they actually work under the hood.

No jargon. No buzzwords. Just the real mechanics of how these systems work.

Whether you’re a developer, technical leader, or just curious about what’s happening under the hood—this guide will give you a clear understanding of Agentic AI architecture.

The 5 Core Components

Every AI agent consists of five fundamental components working together:

Five interconnected components working together.

1. Perception: Understanding the Environment

What it does:

• Gathers information from the environment
• Processes incoming data
• Understands current state
• Detects changes

Transforms raw signals into a structured context for the agent.

Technical implementation:

• API integrations
• Database queries
• Sensor data processing
• Event listeners
• Webhook handlers

Real-world application: Team offsite planning intake

• Captures inbound goal: "Plan a 2-day team offsite in Boston for 10 people, budget $5,000, dates March 15–16"
• Collects constraints: budget, target dates, city, dietary needs
• Checks organizational calendars for conflicts
• Retrieves team preferences and past venue history
• Extracts entities from the brief (headcount, dates, budget, location)
• Emits a normalized context object for reasoning/planning

2. Reasoning: Making Sense of Information

What it does:

• Analyzes perceived information
• Evaluates options
• Assesses risks and benefits
• Makes decisions

Turns context into a clear recommendation.

Technical implementation:

• Large Language Models (LLMs)
• Decision trees
• Rule engines
• Probability models
• Cost-benefit analysis

Real-world application: Team offsite reasoning:

• Goal requires venue + catering + schedule
• Budget $5,000, headcount 10, dates March 15–16
• Options: Hotel A, Coworking B, Retreat C
• Trade-offs: price vs amenities vs location
• Recommendation to planning: Evaluate Hotel A and Coworking B next

3. Planning: Breaking Down Goals into Steps

What it does:

• Decomposes goals into actionable tasks
• Determines task dependencies
• Optimizes execution sequence
• Allocates resources

Converts recommendations into an executable plan.

Technical implementation:

• Task decomposition algorithms
• Dependency graphs
• Scheduling algorithms
• Resource allocation
• Constraint satisfaction

Real-world application: Planning a team offsite:

• Goal captured: Plan a 2-day team offsite in Boston for 10 people
• Constraints gathered: Budget $5,000, target dates March 15–16, city Boston
• Tasks identified (to be executed later): search venues, check availability, compare options, book venue, arrange catering, send invites
• Dependencies mapped: availability check depends on venue shortlist; comparison depends on availability; booking follows comparison; catering runs in parallel with booking; invites follow booking
• Sequence decided: parallel where possible, sequential where required
• Shortlist finalized and availability checks scheduled
• Responsibilities assigned and timelines estimated
• Success criteria set: confirmed venue, dietary accommodations captured, invites sent by a deadline

4. Action: Executing the Plan

What it does:

• Executes planned tasks
• Interacts with external systems
• Makes changes to environment
• Handles errors and retries

Executes the plan and captures verifiable results.

Technical implementation:

• API calls
• Database operations
• File system operations
• Email/notification sending
• Third-party integrations

Real-world application: Executing tasks for the offsite:

• Calls venue booking API
• Processes payment
• Sends confirmation email
• Creates calendar events
• Updates project management system
• Notifies team members

5. Learning: Improving Over Time

What it does:

• Tracks outcomes
• Analyzes what worked
• Updates models and strategies
• Improves future performance

Uses outcomes to update policies and improve over time.

Technical implementation:

• Reinforcement learning
• Feedback loops
• Performance metrics
• Model fine-tuning
• A/B testing

Real-world application: Learning from offsite planning:

• "Team preferred venues with outdoor space"
• "Booking 2 months in advance got better rates"
• "Friday-Saturday works better than Thursday-Friday"
• "Catering budget should be 20% higher"
• Next time: Prioritize these preferences

Real Examples with Technical Breakdown

Example 1: Planning a Team Offsite

End-to-end agentic workflow for planning a team offsite.

User Request: “Plan a 2-day team offsite in Boston for 10 people, budget $5000, dates March 15-16”

AI Agent Process:

Example 2: Publishing a Blog Post

End-to-end agentic workflow for publishing blog posts.

User: “Publish weekly blog posts about AI trends every Monday at 9 AM”

AI Agent Process:

The Architecture Explained

Simplified Architecture Diagram

The Governance Layer (Wraps Everything)

The Governance Layer (Critical!)

Every Agentic AI system MUST have appropriate governance:

1. Clear Boundaries

Define what the agent CAN do:

• Search venues and compare options
• Book hotels up to $200/night for 2 nights (Double occupancy)
• Arrange catering up to $50/person
• Send calendar invites
• Track RSVPs and confirmations
• Budget flexibility: Can exceed by 10%

Define what REQUIRES approval:

• Hotel bookings over $200/night for 2 nights
• Catering over $50/person
• Total spend exceeding overall budget threshold + 10%
• Venue changes after confirmation
• Contract modifications

Define what is FORBIDDEN:

• Commit to contracts without legal review
• Override team preferences or constraints
• Book without budget confirmation
• Make binding commitments beyond authority
• Share confidential team information

2. Human Oversight

Oversight levels:

3. Audit Trails

Every action must be logged:

• Who triggered the action (user/agent)
• What action was performed
• When it occurred
• What data was accessed/modified
• What the outcome was
• Any approvals or overrides

4. Kill Switches

Emergency controls:

• Pause all agent actions
• Rollback recent changes
• Alert human operators
• Preserve system state
• Enable manual override

Building Your First AI Agent

Step 1: Define the Goal

Good goal: “Monitor customer support tickets and automatically respond to common questions”

Bad goal: “Use AI for customer support”

Step 2: Identify Required Components

Perception:

• Monitor ticket queue
• Read ticket content
• Check customer history

Reasoning:

• Classify ticket type
• Assess complexity
• Determine if auto-response appropriate

Planning:

• Select response template
• Personalize message
• Schedule follow-up if needed

Action:

• Send response
• Update ticket status
• Log interaction

Learning:

• Track customer satisfaction
• Identify gaps in knowledge base
• Improve response quality

Step 3: Build the Tool Layer

Required integrations:

• Support ticket system API
• Customer database
• Knowledge base
• Email/notification system
• Analytics platform

Step 4: Implement Governance

Boundaries:

• Can respond to: FAQs, simple issues
• Must escalate: Complex problems, complaints
• Cannot: Make refunds over $100, change policies

Oversight:

• Human reviews 10% of responses
• Escalates if confidence < 80%
• Weekly performance review

Step 5: Deploy and Iterate

Deployment Timeline

The Bottom Line

Agentic AI isn’t magic. It’s sophisticated orchestration of:

• Perception (understanding the world)
• Reasoning (making sense of it)
• Planning (figuring out what to do)
• Action (doing it)
• Learning (getting better)

All wrapped in appropriate governance and human oversight.

That’s what we’re building at FLYTEBIT TECHNOLOGIES—AI agents that understand your business, act on your behalf, and improve over time.

Want to See This in Action?

We’re building AI agents that automate complex workflows for development teams.

Stay tuned for our upcoming product launch.

Ready to get started?

• Visit us at: flytebit.com
• Follow FLYTEBIT TECHNOLOGIES on LinkedIn for insights and updates
• Schedule a free consultation to discuss your AI agent strategy

Related Reading

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👉 The 6 Biggest AI Implementation Mistakes (And How to Avoid Them)

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Understand the business impact:

👉 Why Agentic AI Matters Now - More Than Ever

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Key Takeaways

• ✅ 5 core components: Perception, Reasoning, Planning, Action, Learning
• ✅ Key difference: Goal-oriented vs command-driven
• ✅ Critical: Governance layer for safety and accountability
• ✅ Approach: Start small, prove value, scale gradually