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AI Agent · Autonomous · MCP

Agent

Standalone AI agent binary for autonomous task execution. MCP integration, session management, and plan execution.

Features

Autonomous Execution

Runs tasks without human supervision. Plan-based execution with checkpoints, rollback on failure, and progress reporting via MCP.

MCP Integration

Native Model Context Protocol support. Exposes tools, receives instructions, and communicates with other agents and services over a standard protocol.

Session Management

Persistent session state across invocations. Resume interrupted work, maintain context between steps, and track execution history.

Plan Execution

Reads structured plans and executes them step by step. Supports task dependencies, parallel execution where safe, and progress tracking.

How It Works

1

Receive a Plan

The agent receives a structured plan via MCP or a local file. Plans define tasks, dependencies, and success criteria.

2

Execute Autonomously

Each task runs in isolation with its own context. The agent uses available tools, calls APIs, and manages files as needed.

3

Report Results

Progress and results are reported back via MCP. Failed tasks include diagnostics, successful tasks include artefacts.

Use Cases

CI/CD Pipelines

Automated code review, testing, and deployment as part of your existing pipeline.

Content Scoring

Batch ethical scoring of content libraries using LEM models and the EaaS API.

Infrastructure

Monitor services, respond to alerts, and execute runbooks without manual intervention.