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
90 entries mentioning it- HighCursor 0.45: background agents autonomously fix bugs and open PRs from GitHub issues
- HighAnthropic releases Memory API GA for Claude: structured persistent storage for agents across sessions
- HighMicrosoft Build 2026: Copilot becomes the agentic OS layer for Windows
- HighOpenAI Codex 2.0: dedicated autonomous coding agent in ChatGPT and API
- LandmarkAnthropic releases Claude Opus 4.8: the most powerful Claude model to date
- MediumMistral releases Devstral Small: 7B coding model for agentic tasks on consumer GPU
- HighServiceNow Now AI Agents GA: autonomous IT service management at scale
- LandmarkGPT-5.5: OpenAI shifts ChatGPT toward an "agent runtime" paradigm
- HighDeep Research and Deep Research Max: Google's autonomous research agents with MCP
- HighSAP AI Foundation 2026: Autonomous AI Agents Embedded Across ERP Workflows
- HighCursor 3: the IDE becomes a control room for parallel agents
- HighOpenAI consolidates its agent platform: Operator and ChatGPT Agent merged
- HighSalesforce Agentforce 3.0: AI agents autonomously handle full sales cycles in CRM
- HighGitHub Copilot Coding Agent: model picker, self-review, and built-in security scanning
- HighElevenLabs launches Studio Enterprise: voice cloning with consent verification and 200+ languages
- 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