engineers.do
Autonomous Engineering Agents
Deploy engineering agents that manage infrastructure, automate deployments, and resolve incidents. Schedule maintenance tasks and scale operations efficiently.
import { Engineer } from 'engineers.do' // Monitor and auto-scale infrastructureEngineer.every.day($ => { const health = $.Infrastructure.analyze.Metrics() if (health.cpuUsage > 80) $.Kubernetes.scale.Up()}) Engineer.on.Alert.triggered($ => { $.Incident.diagnose().then(d => $.Runbook.execute(d))}) Engineer`optimize database indexes for orders table`
Frequently asked questions
Engineers.do agents make independent infrastructure decisions, execute deployment pipelines, auto-scale resources, and resolve common incidents without human intervention. Configure safety boundaries (e.g., max spend limits, require approval for production changes), monitor all actions in real-time, and let agents handle 80%+ of routine operations. Agents learn from historical incidents and improve response patterns over time.
Agents can deploy applications, scale infrastructure, rotate secrets, analyze logs, optimize database queries, run security scans, execute runbooks, manage CI/CD pipelines, provision cloud resources, and coordinate multi-service deployments. They understand infrastructure-as-code patterns, can read Kubernetes manifests, Terraform configs, and Docker files with full context awareness.
Set explicit operational boundaries: max infrastructure cost per action, require human approval for production deployments, restrict access to specific environments, and define rollback criteria. Agents operate in dry-run mode by default for new configurations. Full audit logs capture every decision with reasoning, complete rollback capabilities for all changes, and integration with approval workflows (Slack, PagerDuty, custom webhooks).
Yes. Engineering agents coordinate via semantic protocols using $.Agent.delegate.Task patterns. For example, Engineer agents can delegate security scans to SecurityAgent, request database optimizations from DBA agents, or coordinate with SRE agents for complex incident response. Build multi-agent systems where specialized agents collaborate on infrastructure objectives.
Choose from GPT (best for code generation), Claude (best for reasoning about infrastructure), Gemini (best for analyzing logs), or bring your own specialized models. Agents intelligently route engineering tasks based on model strengths: GPT for Terraform generation, Claude for incident diagnosis, Gemini for log analysis. Automatic model fallbacks, streaming for real-time feedback, and cost optimization included.