ASAP’s sophisticated AI Assistants coordinate multiple specialized AI agents to dynamically automate tasks into workflows.
ASAP’s AI agents can form dynamic teams and incorporate feedback from humans or other AI agents to improve performance and optimize workflow outcomes.
Reasoning and Planning
ASAP AI Agents can reason, plan, and use tools that interact with the external environment. This capability enables effective interaction with complex environments, autonomous decision-making, and assistance with a wide range of tasks.
The close synergy between "acting" and "reasoning" allows ASAP AI Agents to quickly learn new tasks and make robust decisions, even in unfamiliar situations. Moreover, ASAP AI Agents can adjust their plans based on new feedback or information.
The agents' reasoning skills help prevent misinterpretation of queries, avoid generating responses based on a purely literal understanding, and ensure they consider multi-step implications.
Effective Tool Calling
ASAP's multi-agent architecture solves challenging tasks by combining reasoning with tool-calling steps. By utilizing multiple tools, the agents can interact with external data sources, send or retrieve information from existing APIs, and more.
To tackle complex tasks, ASAP AI Agents break down a larger problem into smaller tasks, solving each one with the appropriate tools either in sequence or in parallel. Each agent independently addresses a specific task, using its own set of tools to achieve the solution.
ASAP Multi Agent Architecture
ASAP's multi-agent architecture works in stages, where teams of agents are dynamically created and reorganized for each phase of planning, execution, and evaluation. This dynamic reorganization delivers superior results by deploying specialized agents for specific tasks and removing them when no longer needed. By aligning agents' roles and skills with the task at hand, these teams achieve greater accuracy and reduce the time needed to reach the goal.
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