AI Frameworks & Agent Orchestration - Google ADK
Google ADK is powerful and
unforgiving if you skip the architecture work.
We design and build multi-agent systems on Google's Agent Development Kit - wiring Gemini models, tool calling, session state, and evaluation into workflows your operations team can actually run and maintain.
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$250M+
Pipeline generated
42%
Average pipeline growth
18.3%
Average budget saved
Results from actual client engagements.
Trusted by the teams we build with
Most ADK builds stall between a working demo and a production system.
Google ADK gives you a serious orchestration framework - sequential agents, parallel agents, loop agents, tool calling via function declarations, built-in evaluation with the ADK eval harness, and tight integration with Vertex AI and Gemini. That surface area is also where teams get stuck. Agent graphs that work in a notebook break when session state isn't managed correctly across turns. Tool definitions that look clean in isolation fail silently when the model misroutes between sub-agents. Evaluation pipelines get skipped because setting up test datasets against the ADK eval schema feels like a second project. The result is a demo that impresses leadership and a codebase nobody wants to put in front of a customer.
Revenue Institute treats ADK as an engineering problem with a business outcome attached. We scope the agent graph before writing a line of code, define tool contracts that the model can reliably call, build session and memory handling that survives real conversation length, and wire the ADK eval harness to a dataset drawn from your actual use case - not synthetic examples. We also set up the deployment path on Cloud Run or Vertex AI Agent Engine so the system has a real operational home, not just a local runner.
What we do with Google ADK
What we build inside your Google ADK deployment.
Agent graph design and orchestration
We map your workflow to ADK's orchestration primitives - SequentialAgent, ParallelAgent, LoopAgent, and custom BaseAgent subclasses - before any code is written. The graph reflects your actual business logic, not a generic chatbot pattern, so routing decisions are predictable and auditable from day one.
Tool and function declaration architecture
ADK tool calling lives or dies on how function declarations are written. We define tool schemas that Gemini can reliably interpret, build the underlying Python callables with proper error handling and return typing, and test tool selection behavior across edge cases so the model routes correctly in production, not just in the happy path.
Session state and memory management
ADK's session service and state dictionary give you the building blocks for multi-turn memory, but the wiring is manual. We implement session persistence - using the built-in InMemorySessionService for lower-stakes flows or a database-backed service for production - so context survives across turns and agents share state without collisions.
Evaluation pipeline with ADK eval harness
We build an evaluation dataset from your real use cases and wire it to ADK's eval framework, defining trajectory and response metrics that reflect what good actually looks like in your workflow. This gives you a repeatable test suite you can run before every deployment, not just a vibe check from a developer.
Vertex AI and Gemini model configuration
We configure the Gemini model bindings inside ADK - model selection, safety settings, generation config, and grounding with Google Search or Vertex AI Search where relevant - and align those choices to your latency, cost, and accuracy requirements rather than defaulting to the largest available model.
Deployment and operational handoff
We deploy the agent system to Cloud Run or Vertex AI Agent Engine, set up logging through Cloud Logging and tracing through Cloud Trace, and document the operational runbook so your team knows how to monitor agent behavior, update tool definitions, and roll back a bad deployment without calling us.
Our framework
How a Google ADK engagement runs.
Scope and design
We start with the workflow, not the technology. We map the decision points, data sources, and handoff conditions that define your use case, then translate that into an ADK agent graph with defined tool contracts and session requirements. You get a design document before any code is written, so there are no surprises in the build.
Build and evaluate
We build the agent system iteratively, running the ADK eval harness against a real dataset at each milestone. Tool calling, routing logic, and session behavior are tested against edge cases drawn from your actual data - not synthetic prompts. You see working software at each checkpoint, not a final reveal.
Deploy and hand off
We deploy to your target environment, wire up observability, and run a structured handoff that includes documentation, a recorded walkthrough, and a defined support window. Your team leaves knowing how the system works, how to change it, and what to watch in production.
What Google ADK actually gives you - and where it gets hard in practice.
Google's Agent Development Kit is a Python framework for building single and multi-agent systems on top of Gemini models. Its core value is structure: instead of writing your own orchestration logic, you compose agents from ADK's built-in types - SequentialAgent for linear pipelines, ParallelAgent for concurrent execution, LoopAgent for iterative refinement, and BaseAgent for anything custom. Tool calling is handled through function declarations that the model interprets to decide which Python callable to invoke. Session management is handled through a session service that persists state across conversation turns. And the ADK eval harness gives you a framework for defining what good agent behavior looks like and testing against it repeatedly.
The framework is genuinely well-designed for production use cases, but it surfaces real complexity quickly. Tool declaration quality has an outsized effect on model behavior - a poorly typed or ambiguously described function declaration will cause the model to misroute or hallucinate tool arguments, and debugging that is not obvious. Session state management requires deliberate design; the default InMemorySessionService is fine for development but disappears between process restarts, and moving to a persistent backend is a manual step that many teams skip until something breaks in production. Multi-agent routing - deciding which sub-agent handles which input - depends on how agent descriptions and transfer conditions are written, and getting that right requires iteration against real inputs, not just intuition.
Why the evaluation step is the one most teams skip - and why that matters.
The ADK eval harness is one of the framework's most underused features. It lets you define test datasets as trajectories - expected sequences of tool calls and agent responses for a given input - and run them automatically to catch regressions before deployment. In practice, most teams skip this because building a real eval dataset feels like extra work on top of the build itself. The result is an agent that works on the inputs the developer tested manually and fails unpredictably on inputs that look similar but differ in ways the model handles differently. In a customer-facing or operations-critical workflow, that unpredictability is a serious problem.
The teams that get the most out of ADK treat evaluation as part of the build, not a phase after it. They start with a small dataset of real inputs drawn from the actual workflow, define what correct tool calling and correct final responses look like for each, and run the eval harness at every meaningful change. That discipline is what separates an agent that is production-ready from one that is demo-ready. It also makes model upgrades - moving from one Gemini version to a newer one - a testable event rather than a gamble. Revenue Institute builds that eval infrastructure into every ADK engagement because it is the part that determines whether the system is still working six months after we hand it off.
We're vendor-agnostic
Other AI Frameworks & Agent Orchestration platforms we specialize in
Not sure Google ADK is the right fit? We implement and optimize these too - and we'll tell you honestly which one fits your business.
Google ADK questions, answered
How is Google ADK different from just calling the Gemini API directly?
Calling Gemini directly gives you a model. ADK gives you an orchestration layer on top of it - agent graphs, tool calling with function declarations, session and memory management, a built-in eval harness, and integrations with Vertex AI services. If your use case involves multiple steps, tool use, or multi-turn conversation, ADK handles the plumbing that you would otherwise build from scratch and maintain yourself.
Do we need to be on Google Cloud to use ADK?
ADK is open source and can run locally or on any compute platform. That said, it is designed to work well with Vertex AI and Gemini, and the deployment story is cleanest on Google Cloud - specifically Cloud Run and Vertex AI Agent Engine. If you are already on GCP, the integration is straightforward. If you are not, we can discuss what the deployment target looks like for your environment.
We already have a LangChain or CrewAI prototype. Should we migrate to ADK?
Not automatically. ADK has real strengths - the eval harness, the Vertex AI integration, and the structured agent graph primitives - but migration has a cost. We assess what your existing prototype actually does, where it is breaking down, and whether ADK's specific capabilities address those failure modes. Sometimes the answer is migrate; sometimes it is fix what you have.
What does the ADK eval harness actually test?
ADK's eval framework lets you define test cases as trajectories - the sequence of tool calls and responses an agent should produce for a given input - and as final response quality metrics. You can evaluate whether the agent called the right tools in the right order, whether it handled tool errors correctly, and whether the final output meets a defined quality bar. It is not a magic quality detector; it is only as good as the dataset and metrics you define.
How long does a typical ADK build take?
It depends on the complexity of the agent graph and the number of tools involved. A focused single-workflow agent with three to five tools and a clear evaluation dataset can reach a deployable state in a few weeks. A multi-agent system with complex routing, external data integrations, and a larger eval suite takes longer. We scope time honestly at the design phase so you are not surprised mid-build.
Can ADK agents connect to our existing systems - CRM, ERP, internal databases?
Yes, through tool definitions. ADK tools are Python functions with declared schemas, so they can call any API, query any database, or read from any file system your code can reach. We design the tool layer to connect to your existing systems, handle authentication, and return structured data the model can reason over. The tool contract design is where most of the real integration work lives.
What happens after the build is done - who maintains the agent?
We build with handoff in mind. That means documented code, a clear operational runbook, and a structured knowledge transfer before we close the engagement. We also offer ongoing support arrangements for teams that want a partner to handle model updates, tool changes, and eval dataset expansion over time. We are direct about what your internal team will need to own versus what makes sense to keep external.
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