Selfship

This platform is built for teams running AI agents in production who want recurring failures turned into code changes rather than left in a ticket queue. It watches live customer conversations, identifies patterns when multiple users encounter the same problem, and creates a GitHub pull request with evidence supporting the proposed fix.

The workflow connects production observability with the codebase. It observes what customers were trying to accomplish, detects recurring failures, proposes an eval-backed change, and verifies the result against real traffic after the change is merged. Every change remains under human review, with no automatic pushes to the default branch.

Key Features

Observe Live Agent Traffic

The platform analyzes live customer conversations to understand what users were trying to accomplish and whether the agent successfully completed the task.

This provides context beyond basic observability metrics by connecting agent behavior with the actual customer experience.

Detect Recurring Failure Patterns

Not every failed interaction represents a problem that needs a code change. The platform looks for patterns across conversations to distinguish recurring failures from one-off issues.

This helps teams prioritize problems that are affecting multiple customers or repeatedly preventing successful task completion.

Open Eval-Backed GitHub Pull Requests

When a recurring failure is identified, the platform can create a GitHub pull request containing evidence for the proposed fix.

The PR can include:

• Impact information
• Sample customer sessions
• Evaluation scores
• Evidence supporting the proposed change

Teams retain control of the implementation because every pull request still needs to be reviewed and merged.

Verify Fixes Against Production Traffic

The workflow continues after a pull request is merged. The platform checks subsequent real-world traffic to determine whether the fix actually resolved the underlying failure.

This creates a feedback loop from production behavior to code changes and back to production verification.

Human Review and Merge Control

The platform does not automatically push changes to the default branch.

Teams review and merge proposed changes themselves, keeping engineering ownership over what enters the production codebase.

OpenTelemetry-Native

The platform is built around OpenTelemetry, making it suitable for teams that already use OpenTelemetry-based observability in their AI applications.

It is designed to work with stacks built on different LLMs and agent frameworks rather than requiring a specific model or framework.

TypeScript and Python SDKs

TypeScript and Python SDKs are available for integrating the platform into agent applications and existing development workflows.

This provides teams with options for connecting their production agent stack to the failure-detection and verification workflow.

Ephemeral Analysis Sandboxes

Analysis runs in ephemeral sandboxes rather than requiring the platform to permanently operate against the team's codebase.

The platform also states that it does not train on customer code.

Guardrails for Proposed Changes

Teams can apply controls around how frequently the platform proposes changes and where those changes can be made.

Available guardrail options mentioned include:

• Approval rules
• Path restrictions
• Limits on how often changes can be proposed

These controls help teams introduce automated failure analysis without giving an agent unrestricted authority over the codebase.

Built For AI Agent Teams

AI engineering teams can connect production agent behavior to the code changes needed to address recurring failures.

Support-focused agent teams can investigate repeated failures in customer conversations and turn them into reviewable engineering work.

Teams running production agents can use the workflow to move beyond monitoring traces, latency, and cost toward actually resolving behavioral problems.

Enterprise AI teams can use review controls, audit logs, and compliance-oriented capabilities when additional governance is required.

Common Use Cases

Fixing Repeated Agent Failures

Identify situations where multiple customers encounter the same problem and turn the recurring failure into a proposed code change.

Improving Task Completion

Track whether agents successfully complete customer requests and investigate patterns where specific tasks repeatedly fail.

Connecting Observability to Engineering

Move from production traces and failure signals directly into evidence-backed GitHub pull requests rather than creating separate tickets for every recurring issue.

Verifying Agent Improvements

After a change is merged, check real customer traffic to determine whether the failure actually decreased or disappeared.

Auditing Automated Proposals

Keep evaluation evidence, sample sessions, and impact information attached to the pull request so engineers can understand why a change was proposed.

Controlling Automated Code Changes

Use approval rules, path restrictions, and proposal-frequency limits to constrain what the system can recommend and how often it can act.

Why It Matters

Traditional observability can show that an AI agent is slow, expensive, or failing, but identifying a recurring failure does not automatically resolve it. Teams still need to understand the failure, create an engineering task, implement a fix, and determine whether the change worked.

This platform focuses on closing that loop. It connects live agent conversations to recurring failure detection, proposed code changes, evaluation evidence, and post-merge verification.

The emphasis remains on reviewability and control. The system does not push directly to the default branch, does not train on customer code, and uses evidence-backed pull requests so engineering teams can decide what gets merged.

Close the AI Agent Failure Loop

This platform gives AI agent teams a workflow for moving from production failures to reviewable code changes and back to production verification. With live traffic analysis, recurring failure detection, eval-backed GitHub pull requests, OpenTelemetry support, SDKs for TypeScript and Python, and configurable guardrails, it helps teams turn repeated agent problems into an engineering feedback loop rather than another ticket queue.