You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
In 2026, we’ve moved past simple RAG. Our AI agents aren't just reading data; they are executing multi-step infrastructure and business workflows. They are provisioning cloud resources, refactoring legacy schemas, and updating DNS records.
The Problem: Distributed systems are built on determinism. The traditional Saga Pattern relies on predictable "compensation logic" (if Step A fails, run Undo-A).
But what happens when your "operator" is an LLM? If an agent fails at Step 3 of a 5-step deployment, the state is now "dirty." A standard rollback might fail because the agent’s previous actions were non-deterministic, or worse, the agent tries to "fix" the error and creates an Error Cascade.
We are hitting a wall where reliability engineering meets probabilistic intelligence.
How are you handling the "State Mess" when an autonomous agent hits a partial failure in production?
Vote in the poll below and drop a comment on how you handle "Blast Radius" in agentic writes. 👇
Your 2026 Agentic System is executing a multi-step "Write" workflow (e.g., Provision AWS Infra -> Deploy DB -> Migrate Schema -> Update DNS). Step 3 fails. The agent cannot simply "retry" without side effects. How do you architect the rollback when the "o
Deterministic "Reversible" Tools: Enforce a strict undo() method for every tool the agent can access. Trade-off: Massive enginee
0%
The "Draft & Commit" Pattern: Agents perform all actions in a sandboxed "staging" layer; a human or deterministic script "merges
0%
Probabilistic Compensation: Task the agent with "cleaning up its own mess" dynamically.
0%
Global State Snapshots: "Time travel" back to the pre-run state (snapshot restore).
reacted with thumbs up emoji reacted with thumbs down emoji reacted with laugh emoji reacted with hooray emoji reacted with confused emoji reacted with heart emoji reacted with rocket emoji reacted with eyes emoji
Uh oh!
There was an error while loading. Please reload this page.
-
In 2026, we’ve moved past simple RAG. Our AI agents aren't just reading data; they are executing multi-step infrastructure and business workflows. They are provisioning cloud resources, refactoring legacy schemas, and updating DNS records.
The Problem: Distributed systems are built on determinism. The traditional Saga Pattern relies on predictable "compensation logic" (if Step A fails, run Undo-A).
But what happens when your "operator" is an LLM? If an agent fails at Step 3 of a 5-step deployment, the state is now "dirty." A standard rollback might fail because the agent’s previous actions were non-deterministic, or worse, the agent tries to "fix" the error and creates an Error Cascade.
We are hitting a wall where reliability engineering meets probabilistic intelligence.
How are you handling the "State Mess" when an autonomous agent hits a partial failure in production?
Vote in the poll below and drop a comment on how you handle "Blast Radius" in agentic writes. 👇
0 votes ·
Beta Was this translation helpful? Give feedback.
All reactions