For nine hours one night, Conol.ai’s agents stopped processing work. Nothing crashed loudly. No alert fired. Queued tasks simply sat there while the worker process kept breathing — holding open handles, doing nothing. The trigger was a transient database failure during an RDS restart. The loop that drained the queue stopped, but the process didn’t die, so no supervisor restarted it.
What saved them wasn’t the agent runtime. It was what sat underneath: every piece of agent state, and every effect the agents had emitted, was durable in Postgres. When they restarted the worker, queued work resumed from where it had stopped. No sessions reconstructed from scratch. No conversations replayed from logs.
That incident is one of the clearest illustrations of a gap that trips up almost every team moving AI agents to production: the gap between “the model is smart” and “the system is reliable.” Closing that gap is not a prompt engineering problem. It’s a distributed systems problem.
The Problem with Vanilla Agent Runtimes
Here’s how most agent implementations start:
async function runAgent(input: string) {
const messages = [{ role: "user", content: input }]
while (true) {
const response = await llm.chat(messages)
messages.push(response)
if (!response.toolCalls?.length) return response.content
for (const call of response.toolCalls) {
const result = await runTool(call)
messages.push({ role: "tool", content: result })
}
}
}
(Example adapted from Conol.ai’s August 2026 technical post on fault-tolerant agent design.)
This works fine on a laptop. In production, it has one fatal property: all important state lives on the stack and in local variables. The message history, the loop position, the half-finished tool call — these exist only inside one process. If that process dies, hangs, or gets OOM-killed, the entire agent turn goes with it. The model can be as capable as you like; the runtime around it is only as reliable as the weakest process holding its stack.
The mental model that helps bridge this gap comes from an old corner of programming language theory: algebraic effects. You don’t need to implement algebraic effects. But the separation they describe — between what to do and how to do it — is exactly what a durable agent runtime needs.
Primitive 1: State Checkpointing Before Tool Calls
The first primitive is simple: before executing any tool call, checkpoint the current agent state to durable storage.
The checkpoint should contain:
- The complete message history up to this point
- The specific tool call that’s about to execute (name + arguments)
- A unique turn ID
- Enough metadata to resume if interrupted
// Pseudocode — do not use as-is; adapt to your storage layer
async function durableRunAgent(input: string) {
const turnId = generateTurnId()
let state = await loadOrCreateState(turnId, input)
while (true) {
const response = await llm.chat(state.messages)
if (!response.toolCalls?.length) {
await markTurnComplete(turnId, response.content)
return response.content
}
for (const call of response.toolCalls) {
// Checkpoint BEFORE executing the tool
await saveCheckpoint(turnId, {
messages: state.messages,
pendingCall: call,
status: "pending"
})
const result = await runTool(call)
// Record completion AFTER the tool returns
await saveCheckpoint(turnId, {
messages: [...state.messages, response, { role: "tool", content: result }],
pendingCall: null,
status: "completed"
})
state.messages.push(response, { role: "tool", content: result })
}
}
}
On restart, the runtime checks for pending checkpoints. If a checkpoint exists with status: "pending", the tool call may have partially executed or not executed at all. How you handle this depends on whether the tool is idempotent (see next section). If status: "completed", you can safely replay the message history from there.
The key insight: an agent turn is durable data, not a call stack. Treat it that way from the start.
Primitive 2: Idempotency Keys for External API Calls
Checkpointing tells you what was happening when a failure occurred. Idempotency keys tell you whether an external operation actually completed before the failure.
The problem: if your agent calls an external API (send email, create a GitHub issue, trigger a deploy), and the process crashes after the API call returns but before the result is stored, you don’t know on restart whether the call succeeded. Re-executing it might create duplicate actions.
The solution is to assign a stable idempotency key to each external operation, derived from the agent turn context:
idempotency_key = hash(turn_id + tool_name + argument_hash)
When you call the external API, include this key. Most modern APIs (Stripe, GitHub, many SaaS platforms) support idempotency keys — if you send the same key twice, the second request returns the result of the first rather than executing again.
For APIs that don’t support idempotency keys natively, you need to check your own storage before executing:
// Before each external tool call
const existing = await db.findOperation({ idempotencyKey })
if (existing?.status === "completed") {
// Safe to use the cached result — operation already completed
return existing.result
}
// Execute the operation
const result = await callExternalApi(args)
// Store with idempotency key
await db.saveOperation({ idempotencyKey, result, status: "completed" })
return result
This pattern eliminates a class of agent bugs that are extremely difficult to debug: the agent that sent three emails to the same customer because a retry loop didn’t check whether the first send succeeded.
Primitive 3: Compensating Transactions for Irreversible Actions
Some tool calls cannot be made idempotent — they have side effects that can’t be deduplicated. For these, the pattern from distributed systems is the compensating transaction: a defined inverse operation that undoes the effect.
Before executing any irreversible action, the agent registers the compensating action:
// Register compensation before executing
await registerCompensation(turnId, {
type: "delete_github_pr",
prNumber: 47,
repo: "myorg/myrepo"
})
// Execute the irreversible action
await createGithubPR({ ... })
If the overall agent turn needs to be aborted — because a later step failed unrecoverably, or because the agent was instructed to roll back — the runtime executes the compensating actions in reverse order.
This is the saga pattern applied to agent execution. It doesn’t make irreversible operations safe by itself, but it gives you a mechanism for recovery that doesn’t require human intervention for every failure.
Primitive 4: Tiered Error Handling
With the above infrastructure in place, you can implement principled error handling with three tiers:
Tier 1 — Retry: Transient failures (network timeouts, rate limits, temporary service unavailability). Retry with exponential backoff. Most failure modes belong here.
Tier 2 — Compensate: Failures that occurred mid-sequence after irreversible actions were taken. Execute compensation transactions to restore the pre-action state, then surface the error to the agent or user.
Tier 3 — Abort: Failures that cannot be compensated and require human intervention. Log the full checkpoint state, halt the agent, alert the operator. Do not silently swallow these.
The trap is defaulting everything to Tier 1. An agent that retries a failed deleteDatabase() call three times is not being resilient — it’s being dangerous. Build explicit logic for classifying failures into tiers before writing retry handlers.
Putting It Together: What “Durable” Actually Means
The Conol.ai incident is instructive because the failure was banal. A database restart. No cascading infrastructure collapse. The system survived because the agents’ state was in Postgres, not in process memory.
That’s the practical test for whether your agent runtime is durable: if I kill the process right now, with work in flight, what happens? If the answer is “we lose that work and nobody knows,” you have the vanilla problem. If the answer is “the work resumes from the last checkpoint on restart,” you have something production-grade.
The patterns above aren’t novel — they’re the same primitives distributed systems engineers have used for decades: write-ahead logs, idempotency keys, sagas. What’s new is applying them to agent turns, not database transactions. The framing is different, but the engineering is the same.
Start with checkpointing. It’s the highest-leverage first step, and everything else builds on it.
Sources
- Conol.ai — Building agents that survive their own execution
- Hacker News discussion — Building agents that survive their own execution
Researched by Searcher → Analyzed by Analyst → Written by Writer Agent (Sonnet 4.6). Full pipeline log: subagentic-20260801-2000
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