Building iPathPilot: From AI Agent Theory to a Multi-Agent SystemMy Learning Journey / Project OverviewThe AI Agents Intensive fundamentally reshaped how I think about building intelligent systems. Prior to the course, I viewed LLM-based solutions largely as . The course made one thing very clear:Agents are systems, not models.To internalize this shift, I built iPathPilot – an Intelligent Multi-Agent Trip Planning System, a fullstack application that demonstrates how autonomous agents can collaboratively reason, plan, act, and observe in the real world.iPathPilot solves a practical problem:End-to-end trip planning with route optimization, POI discovery, cost estimation, and environmental impact analysis—delivered through a coordinated team of AI agents.Google Agent Development Kit (ADK)Google Maps & Routes APIsServer-Sent Events (SSE) for real-time agent observabilityRather than building a single “do-everything” agent, I intentionally designed a 7-agent sequential pipeline, mirroring real-world organizational workflows.Key Concepts / Technical Deep Dive1. From Monolithic Prompts to Collaborative AgentsOne of the most impactful concepts from the course was multi-agent system design.In iPathPilot, each agent has:The pipeline looks like this:Prompt Capture Agent – Preserves raw user intent (no mutation)Router Agent – Calls Google Routes API (traffic-aware, waypoint optimization)Planner Agent – Converts raw route data into human-readable navigationOptimizer Agent – Identifies traffic, toll, and detour optimizationsPOI Finder Agent – Discovers hotels, restaurants, attractions, fuel stationsCost Calculator Agent – Estimates fuel cost and CO₂ emissionsSummarizer Agent – Produces a clean, structured, frontend-safe responseThis architecture directly reflects Level 3: Collaborative Multi-Agent Systems, where agents treat other agents as tools rather than competitors.2. Tool Use Is Where Agents Become “Real”The course reinforced that tools are an agent’s hands.The Router Agent uses compute_routes() via Google Routes APIThe POI Finder Agent uses Google Search groundingThe Summarizer Agent invokes a custom tool:clean_and_format_route_response()This function was critical. It:Extracts encoded polylinesRemoves control characters (0x00–0x1F, 0x7F)Produces JSON-safe structured outputWraps results in markdown for reliable frontend parsingThis directly applied the course principle:The agent never reasons about how sanitization works—only when it’s needed.3. Observability Is Not Optional for AgentsOne of the strongest lessons from the Agent Quality and Prototype-to-Production modules was:You cannot evaluate what you cannot observe.iPathPilot was designed with observability from day one:Server-Sent Events (SSE) stream each agent’s progressFrontend displays: Intermediate reasoning stagesThe final response is assembled only after all agents complete successfullyTrajectory-based evaluationProcess visibility over final-output-only judgmentDebugging issues like “Why didn’t the polyline render?” became trivial because the agent trajectory was fully visible.4. Agent Quality Over “It Works”The course reframed quality as a continuous loop, not a test case.In iPathPilot, quality is evaluated across four dimensions:Effectiveness – Did the plan match user intent?Efficiency – Were unnecessary agent steps avoided?Robustness – Did the system degrade gracefully on API failures?Safety – Are outputs sanitized and frontend-safe?This thinking influenced:Defensive API error handlingExplicit agent instructions (e.g., “MUST call tool X”)The biggest mindset shift:An agent that “eventually works” can be improved a lot.5. From Prototype to Production MindsetiPathPilot is not a demo – it is deployable.Key production considerations baked in:Cloud Run & Vertex AI Agent Engine deploymentEnvironment-based configurationOptional AgentOps tracingFrontend-backend contract enforcementThe course made it clear that 80% of the work happens after the agent is “intelligent enough.”Agents are autonomous systems, not enhanced promptsObservability is foundational, not an afterthoughtTool design and documentation matter more than prompt clevernessEvaluation must focus on behavior and trajectory, not just answersHow has my understanding evolved?“How do I get the model to respond correctly?”“How do I design a system that reasons, acts, fails safely, and improves?”What I would do differently next timeIntroduce parallel agent execution earlierAdd RAG + MCP from day oneBuild automated evaluation gates into CI/CDBuilding iPathPilot transformed abstract agent concepts into concrete engineering practice. The AI Agents Intensive didn’t just teach how agents work—it taught > how to trust them in productionIf you’re transitioning from LLM demos to real-world systems, my biggest advice is this:Design agents like you design teams: with clear roles, visibility, accountability, and trust.If you’d like to explore the project or extend it (bookings, voice AI, group travel, or enterprise logistics), check out the repository and feel free to contribute.Happy building in the agentic era.