How Do Models Do More Than Just Answer Questions?
So far, we’ve looked at how models are trained, how you prompt them, and how they retrieve helpful context. Now let’s explore the next level of capability: what happens when models begin to act, plan, or chain together multiple steps. This section covers application patterns—ways AI systems are built to move beyond answering a single prompt. These patterns allow models to assist with real workflows, reason across steps, use tools, and even operate semi-autonomously.Agent
What it means: An agent is an AI system that can take actions on your behalf by observing, planning, and executing steps toward a goal you define for it. Analogy: If a single prompt is like asking a question, an agent is like assigning a task. Agents decide what steps to take and in what order to complete something more complex. Use case: You ask an agent to “prepare a report on our website traffic.” It might search your data, summarize the results, and send you a document—all without another prompt. How it shows up in Gloo: Gloo uses agent patterns inside Chat for Teams and Studio. These allow the system to retrieve documents, summarize content, classify materials, and generate structured outputs without the user needing to prompt every step.Autonomous Agent
What it means: An autonomous agent can operate over longer periods without user input. It can keep track of goals, retry failed steps, and decide what to do next on its own. Why it matters: Autonomous agents move AI from reactive to proactive. Instead of waiting for instructions, they can keep working in the background toward a goal. Caution: Autonomy introduces risk. These systems need clear constraints, guardrails, and oversight to prevent unwanted behaviors. How it shows up in Gloo: Gloo does not currently offer autonomous agents that act without user oversight. Instead, Gloo emphasizes controlled, predictable workflows where the model operates only within the organization’s content, rights boundaries, and safety constraints.Tool Use (or Function Calling)
What it means: Tool Use lets the AI call specific functions or APIs during a conversation. These tools do things the model itself cannot do—like look up real-time data, send messages, or update a calendar. Example: If a user asks, “What’s the weather in Kansas City right now?” the model can call a weather API to get the answer. It didn’t guess—it used a tool. Why it matters: Tool use bridges the gap between AI and software systems. It makes AI incredibly more useful, more accurate, and more connected. How it shows up in Gloo: Gloo uses function calling when users or applications ask questions, the system automatically retrieves relevant documents, enriches content, or performs structured tasks using tools vetted for or designed specifically for the flourishing ecosystem.Orchestration
What it means: Orchestration is the coordination layer that decides what the model (or agent) should do next. It might involve chaining multiple tools together, switching between models, or managing memory across steps. Analogy: Think of orchestration as a conductor managing a band of agents, models, and tools—making sure each one comes in at the right time and in the right order. Use case: In a customer service flow, orchestration might route one request to a document summarizer, another to a live agent, and another to a scheduling assistant—all automatically. How it shows up in Gloo: Gloo orchestrates multiple backend steps such as chunking documents, running embeddings, performing semantic search, and assembling grounded responses. This orchestration ensures each response is aligned, accurate, and sourced from the organization’s content and content to which they have access rights.Chain of Thought (CoT)
What it means: Chain of Thought refers to the model reasoning step-by-step rather than jumping to a final answer immediately. It is often prompted with intermediate steps to improve accuracy. Why it helps: Just like people solve problems by breaking them into steps, models can be encouraged to “think aloud” before answering. This improves reliability for complex tasks like math or logic.Memory
What it means: Memory refers to the model’s ability to retain relevant information between interactions. Unlike the context window (which is temporary), memory can persist across sessions. Use case: A chatbot with memory might remember your name, preferences, or past questions—even days later. Note: Memory is often implemented using external storage (like a database), not inside the model itself. How it shows up in Gloo: Gloo Chat for Teams uses memory to constantly improve responses for its users. Also, organizational memory is handled through the Data Engine, which stores content, metadata, and past documents that the AI can reference when generating grounded responses.Guardrails
What it means: Guardrails are safety rules or constraints placed around AI behavior. They define what the model can and cannot do, say, or access. Examples:- Blocking certain topics
- Preventing sensitive data from being generated
- Requiring multiple confirmations before triggering actions
Next Up: What Are the Risks and Challenges of AI, and How Can We Use It Responsibly? In the next section, we’ll answer: “What limitations, risks, and safety concerns come with modern AI, and how do we address them with care and responsibility?”