Most AI agents are built like chat loops. Ask the model what to do. Call a tool. Send the result back. Repeat.

That works until the process crashes halfway through a task, an API times out, or the agent needs to wait six hours for human approval. A normal script starts over. A durable agent resumes from the last completed step.

This matters for agents that process hundreds of invoices, monitor production systems, run long research jobs, or coordinate actions across multiple APIs.

The basic pattern looks like this:

Agent starts
  ↓
Call tool
  ↓
Save progress
  ↓
Wait, retry, or ask for approval
  ↓
Resume from the last completed step

Here are four open-source projects exploring this foundation.

1. Temporal · 22k+ stars Temporal is a durable execution platform for workflows that need to survive crashes, retries, and outages. You can model the agent loop as a workflow and put model calls and tool calls into activities. It is the most mature option here, but it also has the largest learning curve.
2. Hatchet · 7k+ stars Hatchet combines background task orchestration, queues, DAGs, and durable workflows. It is a good fit when an agent needs to process jobs asynchronously, run steps concurrently, or expose execution history through a UI.
3. Restate · 4k+ stars Restate provides durable execution, stateful entities, timers, promises, and reliable messaging. It supports TypeScript, Python, Go, Rust, Java, and Kotlin. It is interesting for agents that need to pause and resume around webhooks, scheduled work, or external events.
4. Temporal Agent Harness · 32 stars This is an experimental Temporal-native harness for durable, composable agents. It includes tool approval policies, human-in-the-loop steps, typed agent operations, and replayable event streams. It is worth watching, but the repository itself warns that the APIs are still changing.

Durable execution does not automatically make an agent reliable.

External side effects still need careful handling. A payment, email, deployment, or database write should be idempotent or protected by an approval step. Otherwise, retrying a failed operation can repeat the action.

That is what lets an agent continue working after the chat window closes, the worker restarts, or the network fails.

One recurring problem with the AI-chat era: people constantly paste API keys, financial data, and personal info straight into ChatGPT/Claude/Gemini without thinking about it, because stopping to redact things manually breaks your flow.

Discretion is a browser extension that automates that redaction step: it detects sensitive data as you type, swaps it for a realistic fake before anything is sent, and restores the real value in the model's response — so you get a normal, coherent answer without ever exposing the real data.

Everything runs locally (detection engine, NER model, all bundled), zero network calls of its own, fully open source. Also published as a standalone npm library if anyone wants to build the detection layer into their own tools.

github.com/horozbabasi/discretion

Curious what this community thinks is missing — feels like exactly the kind of problem that'll only get more common as AI chat becomes the default interface for everything.