What are Agent Systems & Tool Use?

Definition
Agent Systems are LLM driven components that repeatedly observe state, reason about actions, call external tools or APIs, and update state until reaching a goal. Tool Use means the model can invoke structured capabilities like databases, search engines, or payment APIs instead of only generating text.
The Core Problem:
Vanilla Large Language Models (LLMs) are excellent at pattern completion and text generation, but they fail in production on three critical fronts. First, they cannot access up to date or private data because their knowledge is frozen at training time. Second, they cannot reliably perform multi step tasks that require planning and state management. Third, they cannot take actions in external systems like updating databases, making payments, or triggering workflows.

Agent systems with tool use exist specifically to close these gaps.

How It Works:
Think of the LLM as becoming the planner while traditional services remain the executors. Instead of the model hallucinating what a database might contain, it calls a real database tool and gets actual results. Instead of guessing at calendar availability, it invokes a calendar API.

The pattern looks like this: the agent receives a user query, the LLM reasons about what information or actions are needed, it calls one or more tools with specific parameters, receives structured results, and either responds to the user or decides it needs more information and calls additional tools.

A Concrete Example:
Consider an internal support agent. A user asks: "Why is my deployment from yesterday failing?" A vanilla LLM would fabricate possible reasons. An agent system would: call a get_recent_deployments tool to fetch actual deployments for this user, identify the failing one, call a get_deployment_logs tool to retrieve error messages, and then synthesize a specific answer based on real data.

✓ In Practice: Companies like Microsoft Copilot and Google Workspace agents use this architecture to connect LLMs with calendar, docs, email, and code systems, turning the model from a text generator into an orchestrator of real services.

💡 Key Takeaways

✓Agent systems solve three LLM limitations: no access to current/private data, inability to perform multi step tasks, and inability to take actions in external systems

✓The LLM acts as planner and decision maker, while traditional APIs and services act as executors of specific capabilities

✓Tool use means the model outputs structured function calls with parameters rather than just generating freeform text

✓Core components include: tool registry with typed interfaces, orchestrator managing the agent loop, state store for conversation history, and safety layer constraining tool access

📌 Examples

1Support agent calling get_recent_deployments and get_deployment_logs tools to diagnose real deployment failures instead of guessing

2Shopping assistant calling product_search, check_inventory, and calculate_shipping tools to provide accurate availability and pricing

3Code assistant calling read_file, run_tests, and commit_change tools to implement and verify code changes in an actual repository

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