Inside an Agentic AI System: Building Autonomous Agents for Travel
How a multi-agent system plans, collaborates, uses tools, reviews its work, and solves real-world travel problems
In my last post, I answered a fundamental question:
What is an AI Agent?
We looked at how an LLM moves from simply generating text to reasoning, taking actions, using tools, and deciding what to do next.
This post takes the next step:
What happens when we use autonomous agents to solve a real-world problem?
I chose travel because even a seemingly simple trip question can require several different types of information. A traveler may need destination context, practical recommendations, weather, images, and language support—and answering well means bringing those pieces together into one coherent response.
That makes travel a useful domain for demonstrating agentic orchestration: the system needs to determine which capabilities are relevant, delegate work to specialized agents, execute tasks, evaluate their responses, retry when necessary, and synthesize the results into a single answer.
To demonstrate the practical power of autonomous agents, I applied Agentic AI to the discovery phase of this workflow. Rather than building another generic chatbot, I designed an agentic platform centered around a familiar, social-first interface. The traveler posts a request, and autonomous agents take it from there. Through dynamic feeds, specialized agents autonomously handle discovery in real time—extracting latent traveler inspiration, surfacing curated imagery, pulling live weather conditions, and providing seamless multilingual translation.
Travel with AI Agents — Agentic AI System
In a true Agentic AI ecosystem, agents aren’t static scripts or hard-coded workflows—they are modular, autonomous, discoverable, observable, verifiable, and governed by design. To make this coordination tangible, I built a micro-blogging interface for AI-powered travel agents that turns the black box inside out. The feed lets users observe agents in real time as they reason, act, delegate, retry, and verify actions to execute end-to-end trip curation.
In the project here, there is just one human-shaped account: Traveler.
Everything else is an agent.
A traveler posts a request and mentions the Travel Agent or Curator.
From that point, the system can autonomously determine:
What needs to be done? ↓ Which agents are relevant? ↓ What should each agent do? ↓ Which tasks can run in parallel? ↓ Are the results good enough? ↓ Should an agent retry? ↓ How should everything be synthesized?
No human manually orchestrates those steps after the initial request.
The project currently includes specialized agents for destination research, practical travel information, weather, destination imagery, and translation. The Travel Agent acts as the orchestrator: it plans the work, delegates tasks, reviews the responses, retries when necessary, and produces the final synthesized answer.
The interesting part is therefore not the social feed itself.
The interesting part is the control loop behind it.
See It in Action
Before getting into the architecture, here’s the system in action.
The video shows a traveler posting a request, the Travel Agent selecting and coordinating specialized agents, agents using their tools, responses being reviewed and retried when necessary, and the final answer being synthesized back into the feed.
High-level Architecture
Getting a single LLM to answer a question is relatively straightforward. The more interesting engineering challenge begins when multiple autonomous agents need to work together.
You need to answer questions such as:
How does an agent describe what it can do?
How does the orchestrator discover which agents are relevant?
How are tasks delegated and executed?
Which tasks can run in parallel?
How are tool calls handled?
How are agent responses reviewed?
What happens when an answer is not good enough?
How do we observe what the system is doing?
I designed the system around these concerns, separating the traveler experience, orchestration runtime, specialized agents, external tools, and local AI infrastructure.
The architecture below shows the system at a high level.
The Travel Agent sits at the center of the orchestration layer. It does not perform every task itself. Instead, it examines the available agent capabilities, creates a plan, delegates work to relevant specialists, executes independent tasks in parallel where possible, reviews their responses, and synthesizes the final result.
Each specialized agent has its own definition, instructions, model, and tools. This makes agents modular rather than embedding every capability inside one large prompt or workflow.
The external tools provide the connection to real information sources such as Wikipedia, Wikivoyage, Wikimedia Commons, and Open-Meteo. Weather is implemented through a separate local MCP server, demonstrating how an agent can consume a capability that is decoupled from the main application runtime.
The entire system runs locally using Ollama, with the application, agents, orchestration runtime, and MCP server running on the development machine rather than relying on a paid cloud LLM API.
What Makes This Agentic?
The distinction is not simply that multiple LLMs are involved.
The important part is who decides what happens next.
A traditional workflow might look like:
Request → Wikipedia → Weather → Translation → Response
Every step is predetermined by the developer.
This system instead works more like:
Request → Plan → Select Agents → Execute → Observe → Review → Retry if needed → Synthesize
The Travel Agent determines which specialists are relevant to the request. Independent tasks can then execute concurrently, and each response is evaluated against the task it was actually asked to perform. If the response does not meet the requirement, the specialist receives feedback and can retry within a bounded number of rounds.
That shift—from predefined sequencing to model-driven orchestration—is the central idea behind the project.
The Platform, Not Just the Agents
An individual agent can be relatively simple.
The harder engineering problem is building the runtime around it.
In this project, that runtime provides several important capabilities:
ModularityAgents are defined independently through JSON configurations rather than being hard-coded into one workflow.
DiscoverabilityThe orchestrator reads agent descriptions and capabilities to determine which specialists are relevant to a request. These definitions serve a role conceptually similar to Agent Cards, although the project does not implement the formal A2A protocol or its discovery endpoints.
ObservabilityThe application exposes live agent activity so the user can see which agents are working and what stage the system has reached.
VerificationThe Travel Agent reviews specialist responses and can send feedback for another attempt rather than blindly accepting the first result.
Bounded executionTool calls and review iterations have explicit limits, preventing an agent from continuing indefinitely.
These mechanisms turn a collection of model calls into something closer to an agent runtime.
Conclusion
Inside an Agentic AI System is ultimately less about building the application and more about understanding what changes when software can decide what to do next.
The project brings together planning, delegation, parallel execution, tool calling, MCP, review, retry, and synthesis in a single local system.
The travel domain provides the problem space. The real engineering challenge is the layer underneath: how agents are defined, discovered, orchestrated, observed, evaluated, and kept within explicit execution boundaries.
And that is where I see the next stage of agentic AI engineering evolving.
The question is no longer simply:
Can an LLM generate a good answer?
It becomes:
Can we build a system that reliably decides what needs to happen, coordinates the right capabilities, evaluates the result, and knows when it should try again?
This project is a small-scale exploration of that problem—and a starting point for what a more production-grade agent platform could become.
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