In practice
An agent reads email, writes to a database, sends Slack messages. The hard part is handling errors, infinite loops, cost, and tool security. For simple cases a linear pipeline is more reliable than a real agent.
Related terms
Seen in the wild
80 entries mentioning it- LandmarkGPT-5.5: OpenAI shifts ChatGPT toward an "agent runtime" paradigm
- HighDeep Research and Deep Research Max: Google's autonomous research agents with MCP
- HighCursor 3: the IDE becomes a control room for parallel agents
- HighOpenAI consolidates its agent platform: Operator and ChatGPT Agent merged
- HighGitHub Copilot Coding Agent: model picker, self-review, and built-in security scanning
- HighClaude Sonnet 4.7: more reliable agents and longer task duration
- LandmarkClaude Opus 4.6: 1M context, agent teams, and leadership on Terminal-Bench 2.0
- HighClaude Cowork: Anthropic's desktop agent for non-technical knowledge workers
- HighClaude Skills: packaged capabilities loaded on demand into context
- HighClaude Sonnet 4.5: Anthropic's best model for coding and long-running agents
- MediumCline: the open-source VS Code coding agent that splits Plan and Act
- HighChatGPT Agent: OpenAI merges Operator and Deep Research into a computer-using agent
- HighOpenAI Codex Cloud API: thousands of parallel coding tasks on sandbox repos
- MediumOpenHands 1.0: the open-source heir to Devin goes production-ready
- HighCursor Agent and Background Agents: from autocomplete to cloud coding agent
- HighGitHub Copilot Coding Agent: assign an issue to AI like to a junior dev
- MediumADAS: a meta-agent that invents new AI agent architectures
- MediumJules (Google Labs): async agent that resolves GitHub issues autonomously
- HighGoogle A2A Protocol: open standard for communication between heterogeneous AI agents
- HighGoogle ADK + A2A: open-source framework and protocol for agents that talk to each other