Amico is an AI agent framework that lets users create custom autonomous assistants for various business processes. Through a visual flow builder, users define triggers such as email arrival or webhook events, connect to third-party APIs, and program logic for data processing, classification, and response generation. The platform supports integrations with Slack, Zendesk, Google Sheets, and more, enabling agents to ingest data, perform sentiment analysis, schedule meetings, or generate reports. Agents can run on schedules or react to events in real time, executing tasks without human intervention. Advanced features include conversational conversations, multi-step workflows, and performance analytics dashboards. With built-in security and role-based access, Amico offers a scalable solution for automating repetitive tasks and improving efficiency across teams.

Anda AI lets you design, train, and deploy virtual agents that connect to your tools—email, CRM, spreadsheets, chat apps, and more—and perform rule-based or AI-driven tasks automatically. Define triggers, workflows, and data mappings via a visual builder, then monitor agent performance through real-time dashboards. Use cases include email triage, automated reporting, invoice processing, social listening, and CRM updates, all running 24/7 without coding.

Chat BI is an AI-powered tool designed to assist users in generating high-quality reports and data visualizations automatically through natural language inputs. By simply describing what you need, Chat BI can create large-screen reports that integrate seamlessly into existing workflows. This tool aims to empower users to harness their data effectively without the need for coding or advanced data analysis skills. With its user-friendly interface and advanced capabilities, Chat BI brings advanced business intelligence to users at all levels.

AWS Bedrock Multi-Agent Collaboration is a managed service feature that enables you to orchestrate multiple AI agents powered by foundation models to work together on complex tasks. You configure agent personas with specific roles, define messaging schemas for communication, and set shared memory for context retention. During execution, agents can request data from downstream sources, delegate subtasks, and aggregate each other's outputs. This collaborative approach supports iterative reasoning loops, improves task accuracy, and allows dynamic scaling of agents based on workload. Integrated with AWS console, CLI, and SDKs, the service offers monitoring dashboards to visualize agent interactions and performance metrics, simplifying development and operational oversight of intelligent multi-agent workflows.