u/Interesting_Risk5562

Been digging into why agents that work perfectly in demos fall apart in production. Same failure modes keep showing up:

Tool call times out, agent retries, now you have duplicate Linear issues / Stripe charges

Human approval was granted 10 minutes ago, state already changed, agent executes on stale context

Webhook says it succeeded, it didn't, agent moves on anyway

Agent loops and calls the same tool 6 times

Something breaks mid-workflow, no trace of what actually completed

Currently building infrastructure at a startup to handle this as a layer between the agent and the tool. But before we go deep — how are you handling this today? Duct tape retries? Just accepting the duplicates? Curious what the actual workarounds look like

AI agents are great at proposing actions.

They're terrible at reliably executing them in production.

While building agent workflows across Slack, Linear, and APIs, we kept hitting the same issues:

• failed tool calls
• duplicate retries
• partial execution
• stale state
• zero visibility into what actually happened

So we built Invoke.

Instead of letting agents directly execute actions, Invoke sits between the model and the real world:

• validates actions
• enforces permissions/policies
• safely handles retries
• classifies uncertain outcomes
• prevents inconsistent workflow state
• logs every execution step

Example:
An agent tries creating a Linear issue and sending a Slack notification. The API partially fails. Instead of blindly continuing, Invoke detects uncertain execution, pauses downstream actions, and safely retries/reconciles the workflow.

We're starting with operational workflows and execution reliability for AI agents in production.

Curious if others running agents are hitting similar reliability/debugging problems.