By: Joey Lau
Last updated
GM buildOrs!!
In the past month, GAME Cloud was revamped to give builders more flexibility around defining worker prompts. As a result, many of you have asked for some gudielines on how to define the worker prompts. In this article, we dive deeper into this topic!
In GAME Cloud, tasks are broken down into two main layers:
High-Level Planner (HLP) 🧠 : Thinks strategically and decides what needs to be done.
Low-Level Planner (LLP) ⚡: The action-taker that actually executes the tasks using pre-defined functions and environments.
So, what does an LLP Worker do? It operates inside specific locations (contexts) where it can use functions to interact with the environment. For example:
Location 1 might be Twitter, where the worker posts tweets or replies.
Location 2 could be Discord, where it listens for commands and responds.
Location 3 might be a game world, where it gathers resources or completes missions.
Each worker is assigned functions that define what actions it can take inside its location. If the HLP is the brain, then LLP Workers are the hands and legs that make things happen.
A Reply Worker in GAME Cloud is responsible for processing incoming messages and determining whether to respond based on specific conditions. A well-structured prompt guides the worker in making decisions about whether to engage in a conversation or ignore it. Feel free to mix and match the prompt samples below based on your specific requirements to tailor the Reply Worker’s behavior to your use case!
Objective: Use {{replyCount}} to decide if a tweet should be engaged with or ignored.
Below are some example decisions based on the prompt above:
Tweet: "What's your take on AI in crypto?" ({{replyCount}} = 3 → Engage ✅)
Tweet: "Bitcoin to the moon 🚀" ({{replyCount}} = 20 → Ignore ❌)
Objective: If engagement is approved, generate a relevant reply.
Below are some example decisions based on prompt above:
Tweet: "What do you think about AI in crypto?" ({{replyCount}} = 2) Reply: "AI is revolutionizing crypto! From market analysis to security, it’s making trading smarter. What’s your take?"
Tweet: "Best DeFi projects for 2025?" ({{replyCount}} = 8) Reply: "DeFi is evolving! Keep an eye on cross-chain liquidity & L2 solutions."
Tweet: "Crypto adoption is growing!" ({{replyCount}} = 12)Reply: No reply (conversation too long).
Objective: If engagement is not needed, generate a polite non-reply response or ignore.
Below are some example decisions based on prompt above:
Tweet: "This is my 5th time asking, where’s your roadmap?" (Repeated question, {{replyCount}} = 11) Reply: "Hey! Our roadmap is available here: [link]. Let us know if you have further questions!"
Tweet: "How does GAME SDK work?" ({{replyCount}} = 18 → Ignore (too many replies))Reply: No reply (conversation too long).
✔ Uses {{replyCount}} to dynamically adjust engagement.
✔ Optimizes API usage by reducing unnecessary replies, which is crucial due to API rate limits.
✔ Prevents spam replies while keeping high-value conversations active.
Some agent configurations require static environment variables rather than just prompt-based or goal-based approaches. Here are examples that CANNOT be achieved via prompts and must be declared in Environment JSON:
This section displays the default name of the worker, which is set to Twitter Main Location in this case.
The name indicates that this worker is primarily focused on Twitter-related tasks, such as engagement, content posting, or analysis.
This section provides an overview of what this worker is designed to do.
A single worker can efficiently handle multiple tasks related to tweets. Below are different use case examples with prompt examples that ensure the agent operates effectively in multiple roles within the session.
For brands or individuals looking to identify key influencers, analyze Twitter trends, and actively engage with high-value discussions.
📝 Prompt:
For businesses using Twitter as a support channel while also promoting product updates and monitoring customer sentiment.
📝 Prompt:
While agent’s goals and descriptions are permanently saved in the system, they are still subject to Agent’s interpretation. Over prolonged working sessions, the agent may experience hallucinations or drift in behavior, where its responses gradually deviate from the intended goals due to context accumulation, misinterpretation, or inconsistencies in memory recall.
To mitigate this, the Environment JSON section serves as a static, structured configuration layer that ensures:
Persistent Execution Rules : Unlike session-based prompts that the AI may reinterpret over time, environment variables remain fixed unless manually updated, providing a stable reference.
Strictly Enforced Constraints : AI can flexibly interpret prompts in agent goals, but Environment JSON defines non-negotiable rules that the AI must adhere to, preventing behavior drift.
Over extended working sessions, the agent may gradually shift in behavior or understanding, leading to:
Variations in branding alignment – The AI may interpret guidelines flexibly and generate tweets that diverge from the intended messaging.
Unintended language use – It may include words or phrases that are best avoided, even if they were not the focus of the goal.
Inconsistent hashtag usage – Over time, the agent may use hashtags in an unstructured way, affecting reach and engagement.
📌 Why Prompts Alone May Not Be Enough?
Even with a clear goal such as "Only tweet about Web3 and Blockchain", the AI may still:
Create loosely related content that unintentionally includes words that aren’t ideal for brand perception.
Misinterpret reputational considerations, leading to tweets that, while technically relevant, don’t align with messaging priorities.
Gradually shift its focus over time, as context accumulates and earlier constraints are not actively reinforced.
Solution: Using Environment JSON for Consistent Content Guidance
To ensure that the AI continuously follows messaging best practices, Environment JSON can be used to define content preferences and focus areas in a structured way.
Example Environment JSON Configuration:
How This Enhances Agent’s Response?
Encourages alignment with key topics: Ensures that the AI remains engaged with areas that matter most, such as Web3, Blockchain, and AI.
Promotes clarity in communication: Helps the AI refine its language choices by avoiding words that could lead to misunderstandings or negative associations.
Ensures a consistent social presence: Keeps hashtag usage focused, improving discoverability and audience engagement.
Creates a structured content framework: While the AI still has flexibility in expression, it operates within a well-defined brand approach.
Prettify JSON in the Environment Section
The Prettify JSON button is a formatting tool that helps clean and structure the JSON configuration for better readability and accuracy.
When working with JSON, formatting issues such as missing brackets, incorrect indentation, or trailing commas can cause errors. The Prettify JSON feature automatically:
Corrects indentation: Ensures that keys and values are properly aligned, making it easier to read.
Formats long JSON structures: Breaks down complex configurations into a structured view.
Highlights syntax errors: Helps identify missing commas, unclosed brackets, or incorrectly structured keys.
In summary, Workers (LLP) in GAME Cloud play a crucial role in executing tasks efficiently by handling multiple Twitter-related functions. While agent goals and prompts guide behavior dynamically, the Environment JSON ensures consistency, security, and compliance, preventing behavioral drift over prolonged sessions.
Now it's your turn!💡
Go ahead, experiment with your own prompts and see how your worker performs. What’s next on your roadmap? Would love to hear how you're using this!
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