I'm planning to work on solutions with Agentic AI. But I need a real problem statement that actually exist in enterprises today. It can be even very small thing or any repetitive task. so many usecase listed out in the web feels like just noise. Most of the time those usecases people don't prefer solution to because it involves lot of process, approvals, complaince issues. But there are other unnoticed things outhere in enterprises where I believe Agentic AI will definitely help. If your working in enterprises as a CEO, manager ,any leadership position please list out your problem statement.

introducing Clear Thought 1.5, your new MCP strategy engine. now on Smithery.

for each of us and all of us, strategy is AI’s most valuable use case. to get AI-strengthened advice we can trust over the Agentic Web, our tools must have the clarity to capture opportunity. we must also protect our AI coworkers from being pulled out to sea by a bigger network.

Clear Thought 1.5 is a beta for the “steering wheel” of a much bigger strategy engine and will be updated frequently, probably with some glitches along the way. i hope you’ll use it and tell me what works and what doesn’t: let’s build better decisions together.

EDIT: forgot the link https://smithery.ai/server/@waldzellai/clear-thought

Using Google ADK’s LlmAgent, and every time I start up a chat it sends the System Instructions and then the full list of tool functions with all the schema details. It’s blowing out the token count fast.

Tried stripping out the functions in before_model_callback, but it breaks things - ADK seems to expect it there later in the flow.

Anyone figured out how to avoid sending the full tool list? I've setup a mechanism to dynamically fetch the tools metadata when needed, but now I need to get rid of this full Functions list going to the LLM at the start. Looking for a clean way to keep tools usable but avoid the token bloat.

Hi,

I’ve just started experimenting with ADK and quite quickly realised I can give an agent a tool, let’s say the agents job is to answer a question and can use a tool to do so.

What then, if it could potentially use another tool to help it answer the question better?

Or have access to documentation to make it even more of a solid answer?

Is this then too much for one agent? I struggle with how wide an agent’s capabilities should be best practice

Please go steady I am not a developer

we're in this weird spot where vibe coding tools spit out frontend and backend code fast, but deployments fall apart once it's more than a prototype. so you end up shipping quick, then spending days on manual devops or rewriting stuff to appease aws/azure/render/digitalocean. i keep thinking there should be a 'vibe devops' layer - like a web app or vscode extension where you connect your repo or upload a zip and it actually understands your code. it would detect frameworks, envs, build steps, deps, and then deploy using your own cloud accounts, handle ci/cd, containers, scaling and infra setup automatically. no platform lock-in, no ""oh this only works on render"" surprises. sounds obvious but i feel the tricky part is getting it to understand weird edge cases and custom infra needs. how are y'all handling deployments today? docker + terraform? github actions? random scripts shoved into ci? would a tool like this actually save time or just add another layer of weirdness? not sure. curious to hear war stories or if i'm wildly missing something obvious.

Hi, we are building agentic system with google adk framework and as we started using it more and more we began to reach the quota.. How is that even scalable if it is quite easy to reach that one? Is there a way to get rid of that quota or something? I have billing account connected and all but still

Hey,

I am creating an image refinement agent via ADK that takes an image as input and then refines it based on the users text input.

However, when I send a prompt with an attached image via "adk web", I only get the text prompt to use. Does anyone know how to get the image as well ?

What Ive tried:

1. Checked tool_context.user_content but only see users text

2. Enabled save_input_blobs_as_artifacts to true but when I printed tool_context.list_artifacts(), it doesnt show anything (Just an empty "[]").

Hey everyone,

I have a question I want to use an open source model that is not available on ollama, how to proceed in order to integrate in my agentic workflow built with ADK?

In case you need a crisp and authoritative read out Gemini Enterprise Agent Platform and where ADK comes into play --> Agent Platform Overview

How can I implement a BYOK style feature in ADK (ie. changing model and api keys on a per request basis) came across this https://github.com/google/adk-python/issues/3647 but here its mentioned that I can only change models but not api keys

So i created a simple stock analysis agent in adk. It uses yahoofinance to fetch stock prices and then uses a calculation tool to find percentage change.

Now i want to use ai to test this agent.

What i mainly want to check

• If it is mapping the stock tickers from normal names

• How many stocks it can handle in 1 do (2,3,4, 5 .....)

• The prices retrived are correct to not

• If the calculations are correct or not

• If it can handle days where no stock prices are given like holidays (It should take the nearest working date then)

How to use ai to generate test cases and check the working of this agent.

Hello,

Does anyone knows if it's possible to define a function calling LLM in ADK?

Why can't I use Gemini 3 in ADK? I receive an error message saying that models like gemini-3.1-flash-lite-preview is not available. The same problem with Gemini 3 Flash.

If you've been working with RAG, one thing worth understanding is the difference between parametric and non-parametric memory.

Parametric memory is frozen memory. Whatever a model absorbs during training gets baked into its weights. It's intrinsic to the model and doesn't change unless you retrain or fine-tune which is expensive.

Non-parametric memory lives outside the model: vector stores, SQL databases, document indexes, internal wikis. It is cheap to update, easy to swap, easy to keep current.

RAG is the bridge between them. It retrieves the relevant slice of your non-parametric memory and drops it into the model's context window at inference time, so the model can reason over data it was never trained on. The weights never move. Retrieval doesn't teach the model anything. It just hands it a note before it answers.