> ## Documentation Index
> Fetch the complete documentation index at: https://docs.spineworkspace.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Technical Account Success AI Flow: Twin Technical Reasoning

> The OODA-style AI reasoning flow for Technical Account Success covering observation, orientation, context assembly, and governed action.

The Technical Account Success AI Flow describes how Twin processes technical signals, assembles understanding, and proposes or executes actions on behalf of TAMs. This is the AI Workbench path applied to technical customer management.

<Steps>
  <Step title="Observe">
    Twin monitors event streams from Datadog, Zendesk, Jira, GitHub, and API gateways. Notable signals include integration failures, API error spikes, deployment issues, ticket escalations, and milestone delays.
  </Step>

  <Step title="Orient">
    Twin classifies each signal against technical domain rules. A latency spike on a production integration is oriented as high priority. A minor deprecation warning on a dev environment is oriented as low priority. Orientation considers account tier, environment, and business impact.
  </Step>

  <Step title="Context Assembly">
    For prioritized signals, Twin queries Spine Fabric for full technical context: architecture, integrations, API usage, open issues, and recent deployments. It gathers comparable account patterns.
  </Step>

  <Step title="Reason">
    Twin evaluates possible explanations and outcomes. An integration failure may stem from configuration drift, API version mismatch, rate limiting, or upstream system outage. It weights each hypothesis by evidence.
  </Step>

  <Step title="Discover">
    Twin identifies the best intervention from the capability library: integration remediation, architecture update, technical escalation, or debt scheduling. It checks authorization and scope.
  </Step>

  <Step title="Delegate">
    If the intervention requires specialist skills, Twin delegates to domain agents. The Integration Agent handles root cause analysis. The Architecture Agent assesses design implications. The Escalation Agent coordinates engineering.
  </Step>

  <Step title="Collaborate">
    Twin coordinates with the TAM through the AI Workbench. Proposals appear as drafts with reasoning and source evidence. The TAM reviews, adjusts, or approves before execution.
  </Step>

  <Step title="Decide">
    Upon TAM approval, Twin finalizes the action plan. If human judgment is required, Twin presents options, trade-offs, and recommended path with confidence level.
  </Step>

  <Step title="Act">
    Twin invokes the appropriate capability: creating a Jira ticket, updating architecture docs, escalating to engineering, or scheduling a remediation window. Each action produces a receipt.
  </Step>

  <Step title="Continue">
    Session state, reasoning log, and outcomes persist in Continuity. Twin resumes with complete memory of pending items and unresolved signals.
  </Step>
</Steps>

## Related

* [Technical Account Success Index](/internal-docs/domains/technical-account-success/index)
* [AI Workbench](/internal-docs/domains/technical-account-success/ai-workbench)
* [Agents](/internal-docs/domains/technical-account-success/agents)
* [Execution Flow](/internal-docs/domains/technical-account-success/execution-flow)
* [System Flow](/internal-docs/domains/technical-account-success/system-flow)


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