1 Fig1 et7jsZ5max 1000x1000 1
Agents are top of mind for enterprises, but often we find customers building one “super” agent – a jack of all trades – instead creating multiple agents that can specialize and work together. Monolithic agents often crumble under their own weight because of instruction overload, inaccurate outputs, and brittle systems that are impossible to scale.
The good news: A team of specialized AI agents, each an expert in its domain, can deliver higher fidelity, better control, and true scalability.
The challenge: Building robust multi-agent workflows is complex. This is where Google’s Agent Development Kit (ADK) becomes essential. The ADK provides the framework to design, build, and orchestrate these sophisticated agentic systems, leveraging the power of Gemini. In this post, we’ll show you how you can build a multi-agentic workflow using ADK.
Instead of one monolithic agent trying to do everything and getting confused, we’ll break the problem down. We’re building a team of focused specialist agents, each with clear instructions for a single job. In this case, we’ll take a travel example:
FlightAgent: Knows only about flights.
HotelAgent: An expert in accommodation.
SightseeingAgent: A dedicated tour guide.
To manage these specialists, build a coordinator workflow. Then, create a TripPlanner root agent whose only job is to understand a user’s request and route it to the correct specialist.
While this works beautifully for simple queries (e.g., “Find me a flight to Paris” is immediately dispatched to the FlightAgent), a new problem quickly becomes apparent. When asked, “Book a flight to Paris and then find a hotel,” the coordinator calls the FlightAgent and stops. It has done its job of routing the initial request, but it cannot orchestrate a multi-step workflow. The manager is a great receptionist but a poor project manager
This limitation stems from how the system handles sub-agents. When the Root Agent calls the Flight Agent as a sub-agent, the responsibility for answering the user is completely transferred to the Flight Agent. The Root Agent is effectively out of the loop. All subsequent user input will be handled solely by the Flight Agent. This often leads to incomplete or irrelevant answers because the broader context of the initial multi-step request is lost, directly reflecting why the manager struggles as a “project manager” in these scenarios.
The coordinator needed an upgrade. It shouldn’t just forward a request; it needed the ability to use its specialists to complete a bigger project. This led to the next evolution: the Dispatcher Agent with Agent Tools.
Instead of treating the specialists as destinations, we will treat them as tools in the root agent’s toolbox. The root agent could then reason about a complex query and decide to use multiple tools to get the job done.
Using the ADK, the specialized agents are converted into AgentTools.
This is a game-changer. When the complex query “Book a flight to Paris and then find a hotel” is run, the root agent understands and it intelligently calls the flight_tool, gets the result, and then calls the hotel_tool. It can also suggest two top places to visit using Sightseeing_tool. The to-and-fro communication between the root agent and its specialist tools enabled a true multi-step workflow.
However, as the system worked, an inefficiency became noticeable. It found the flight, then it found the hotel. These two tasks are independent. Why couldn’t they be done at the same time?
The system is smart, but it’s not as fast as it could be. For tasks that don’t depend on each other, they can be run concurrently to save time.
The ADK provides a ParallelAgent for this. We use this to fetch flight and hotel details simultaneously. Then, a SequentialAgent is used to orchestrate the entire workflow. It first gets the sightseeing info , then “fan-out” to the parallel agent for flights and hotels, and finally, “gather” all the results with a TripSummaryAgent.
We now have an optimized workflow. The system is now not only handling complex queries, but it is doing so efficiently. It is close to the finish line, but one final doubt remains. Is the final summary good? Does it always meet the strict quality guidelines?
A feedback loop is needed for the system to review its own work.
The idea is to add two more agents to the sequence:
TripSummaryReviewer: An agent whose only job is to evaluate the summary generated by the TripSummaryAgent. It checks for completeness and structure, outputting a simple “pass” or “fail.”
ValidateTripSummaryAgent: A custom agent that checks the reviewer’s status and provides the final, validated output or an error message.
This pattern works by having agents communicate through a shared state. The TripSummaryAgent writes its output to the trip_summary key, and the TripSummaryReviewer reads from that same key to perform its critique.
With this final piece in place,, our AI system is no longer a single, confused genius but a highly efficient, self-regulating team of specialists. It can handle complex, multi-step queries with parallel execution for speed and a final review process for quality assurance.
Ready to build your own multi-agent workflows? Here’s how to get started:
Explore project source code
Explore the Agent Development Kit documentation to dive deeper into its capabilities and features.
Check out more ADK examples and tutorials on GitHub to see practical implementations.
submitted by /u/Jack_Fryy [link] [comments]
People who are blind or have low vision (BLV) may hesitate to travel independently in…
In honor of Independence Day, Palantir Veterans and Intelligence Community (IC) alums offer reflections on…
In the telecommunications industry, managing complex network infrastructures requires processing vast amounts of data from…
Sakana AI's new inference-time scaling technique uses Monte-Carlo Tree Search to orchestrate multiple LLMs to…
Newly disclosed records show Attorney General Pam Bondi gave cover to not only Apple and…