The basics
An AI agent combines three capabilities: it can observe what is happening in your tools, it can reason about what it observes, and it can take actions in response. These three capabilities (perception, reasoning, and action) form a continuous loop.
Here is what happens when a message arrives in your Slack support channel:
- Perception. The agent detects the new message. It reads the text, identifies the sender, and notes the channel context.
- Reasoning. The agent processes the message using a large language model (like Claude or GPT-4). It determines: “This is a billing question from a customer. It is asking about a double charge. This is a common issue with a known resolution.”
- Action. Based on its reasoning, the agent takes action: it posts a response in the Slack thread with the resolution steps, creates a ticket in Notion for tracking, and logs the interaction.
This loop repeats continuously. The agent is always watching, always reasoning, always ready to act.
The components
Large Language Models (LLMs)
The “brain” of an AI agent is a large language model. This is the same technology behind ChatGPT and Claude, but instead of generating text in a chat window, it powers the agent’s reasoning about events in your tools.
When the agent sees a new Slack message, it sends the message content (along with instructions about its role and available tools) to the LLM. The LLM processes the text and returns a structured decision: what category the message falls into, what action to take, and what response to generate.
The LLM does not “remember” in the traditional sense. Each interaction is processed based on the instructions (called a “system prompt”) and the context of the current conversation. This makes the agent’s behavior predictable and configurable. Change the instructions, and the agent’s behavior changes.
Tool use
Reasoning alone is not useful. The agent needs to take actions. “Tool use” is the mechanism that bridges reasoning and action.
An AI agent has access to a defined set of tools, which are functions it can call to interact with the outside world:
- Read a Slack message to perceive what is happening
- Send a Slack message to respond to a conversation
- Create a Notion page to log information in your project management tool
- Create a GitHub issue to file a bug report
- Read a Google Sheet to pull data for a report
Each tool has defined inputs and outputs. The LLM decides which tool to use based on the situation, provides the required inputs, and the tool executes the action. This is not magic. It is structured function calling with the LLM deciding which function to call and with what parameters.
System prompt (instructions)
The system prompt tells the agent who it is, what it should do, and how it should behave. It is the configuration that shapes the agent’s personality and decision-making:
- “You are a support triage agent for [Company]. When a customer message arrives, categorize it as billing, technical, or general. For billing questions, check if it matches a known resolution pattern…”
- “You have access to the following tools: send_slack_message, create_notion_page, read_google_sheet…”
- “If you are unsure about a classification, escalate to a human team member by tagging @support-lead in the thread.”
The system prompt is what makes one agent a support triager and another a project reporter. Same underlying technology, different instructions.
Context window
An LLM can only process a limited amount of text at once. This is called the context window. For modern models, this ranges from 100,000 to 200,000 tokens (roughly 75,000 to 150,000 words).
The context window determines how much information the agent can consider when making a decision. It includes the system prompt, the current event (a Slack message, a GitHub issue), recent conversation history, and any relevant context retrieved from your tools.
For most business tasks, the context window is more than sufficient. An agent triaging a support message needs the message content, a few hundred words of instructions, and perhaps some conversation history, well within the limits.
Memory and state
Between interactions, an agent needs to remember what has happened. This is managed through external storage: databases, documents, or the tools themselves.
When an agent creates a Notion page or a GitHub issue, that action persists in the tool. When the agent needs to reference past interactions, it can query these tools. This is more reliable than trying to maintain memory within the LLM itself, because external storage is permanent and searchable.
How agents are different from scripts
A script follows a predefined path: “If the message contains ‘password reset,’ send link X.” It cannot handle messages that do not match its rules. It breaks on unexpected input.
An agent uses an LLM to interpret messages. It understands that “I can not log in,” “my password does not work,” “authentication error after I changed my email,” and “locked out of my account” are all variations of the same issue, even though none of them contain the words “password reset.” This natural language understanding is what makes agents useful for tasks that previously required human judgment.
The role of the platform
A platform like ClawStaff handles everything around the LLM:
- Integration layer connecting to Slack, GitHub, Notion, etc.
- Event detection monitoring tools for new messages, issues, and events
- Permission management controlling what each agent can access
- Container isolation running each agent in its own secure ClawCage environment
- Audit logging recording every action for review and compliance
- BYOK routing AI inference through your own model provider credentials
The platform provides the infrastructure. The LLM provides the reasoning. Together, they create an agent that understands your tools and operates within your team’s workflows. Learn more about what an AI workforce looks like when you deploy multiple agents.
What agents cannot do
AI agents are powerful but bounded:
- They cannot access tools they have not been given permission to use
- They can misinterpret ambiguous messages (though less often than rule-based systems)
- They do not have real-time memory between separate sessions unless it is stored externally
- They work best for tasks with clear patterns and defined outcomes
- They should not be used for high-stakes decisions without human oversight
Understanding these boundaries is important for setting appropriate expectations and designing effective agent configurations.