Core Agent Structure
Every ActiveMotion agent is built around four core components: the reasoning loop, the tool orchestration layer, the memory subsystem, and the escalation controller. The reasoning loop is the central execution engine. It receives a request, decomposes it into sub-goals, plans a sequence of actions, executes each action through the tool orchestration layer, verifies outcomes, and iterates until the request is fully resolved or escalation is triggered. The reasoning loop supports configurable strategies including chain-of-thought for complex multi-step tasks and direct action for simple, well-defined operations.
Tool Orchestration Layer
The tool orchestration layer manages all interactions with external systems. Each tool is registered with a typed schema that defines its inputs, outputs, error modes, expected latency, and cost. When the reasoning loop decides to invoke a tool, the orchestration layer handles authentication, request formatting, retry logic, timeout management, and response parsing. Circuit breakers prevent cascading failures when downstream systems are degraded. Tool calls are logged with full input and output payloads for audit and debugging purposes. The orchestration layer supports parallel tool execution for independent sub-tasks and sequential execution with dependency management for ordered workflows.
Memory Subsystem
The memory subsystem provides three tiers of persistent state. Short-term memory holds the context of the current interaction, including the request, intermediate results, and conversation history. Working memory maintains cross-interaction context for ongoing workflows, such as a multi-day approval process. Long-term memory stores the agent's accumulated knowledge about the environment: entity relationships, resolution patterns, exception records, and historical performance data. All memory tiers are queryable through a unified retrieval interface that supports both semantic search and structured queries.
Escalation Controller
The escalation controller governs the boundary between autonomous action and human involvement. It evaluates the agent's confidence at every decision point against configurable thresholds that vary by action type and risk level. Low-risk actions like answering a policy question may proceed with a confidence threshold of seventy percent. High-risk actions like modifying access permissions may require ninety-five percent confidence or explicit human approval regardless of confidence. When escalation is triggered, the controller packages the complete interaction context, the agent's reasoning so far, and the specific point of uncertainty into a structured escalation payload that routes to the appropriate human queue.