Everruns
Long-Running AI Research Agents for Durable Work icon

Long-Running Research Agents

Configure an agent, start execution, wait for results. Infrastructure reliability is handled.

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The Problem

AI agents can now work autonomously for hours or days. Research from METR shows task completion capabilities doubling every 7 months. Week-long autonomous tasks are expected within 2-4 years.

But long-running execution introduces infrastructure challenges:

  • Host machines crash or restart
  • Network connections drop
  • External APIs hit rate limits or have outages
  • Memory limits get exceeded
  • LLM providers experience downtime

When a task runs for 6 hours and fails at hour 5, you lose all that work. Current agent frameworks assume reliable infrastructure that doesn’t exist in practice.

How Everruns Helps

Everruns uses a managed event loop composed from atoms for durable execution. Every step is persisted, so agents resume from where they left off after any failure.

  1. Configure — Define your agent: model, system prompt, tools, constraints.
  2. Start — Fire off the execution. Everruns handles the rest.
  3. Monitor — Real-time streaming shows progress. Check back anytime.
  4. Survive failures — Crashes, restarts, timeouts, API outages - execution continues from the last checkpoint.

Technical Context

Current solutions focus on making agents smarter within sessions. Anthropic’s multi-session harness addresses context window limitations with initializer and coding agents that maintain progress across sessions. OpenAI’s Deep Research handles multi-step web research. These solve the intelligence and memory problems.

Infrastructure reliability is different. When the underlying compute fails, session-level solutions don’t help. Durable execution guarantees require workflow orchestration at the infrastructure level.

Use Case Examples

  • Literature review — Agent searches, reads, and synthesizes papers over several hours
  • Competitive analysis — Agent monitors and compiles data from multiple sources over days
  • Code migration — Agent refactors a large codebase incrementally, surviving machine restarts
  • Data processing — Agent processes large datasets with external API calls that may rate-limit or fail

Further Reading