The Trace Ledger

The ledger is ContextStack's canonical work truth. Every organizational state transition and execution attempt appends a record. Chat can present or steer the work, but the transcript is never the only place where ownership, authority, or outcome exists.

What the destination records

• Signals, assignments, and the accountable Bot
• Commitments, deadlines, state transitions, and terminal dispositions
• Handoff requests and their acceptance, rejection, or renegotiation
• Approval requests, decisions, policy versions, and authority envelopes
• Orders, runs, attempts, tools, artifacts, and gate verdicts
• No-tool cognition and model-routing receipts
• Provider, model, cost, latency, retries, escalation, and constraint profile
• Evidence bundles, external effects, reconciliation, and accepted outcomes
• Learning proposals, promotion decisions, rollback, and autonomy changes

This is more than a run log. A completed cross-Bot outcome should be reconstructable—who owned it, who contributed, why a route was chosen, what it cost, which authority permitted the consequence, and what evidence established acceptance—without reading chat.

Three jobs, one record

Audit. The organization can explain how a lasting effect occurred and which human or pre-authorization allowed it.

Diagnosis. A failed or stalled commitment can be reconstructed from recorded state even when the exact model behavior is not reproducible.

Measurement. Outcomes can be segmented by work class, route, attempt count, quality, operator intervention, and total cost. That is the evidence required to route cheaper intelligence safely and graduate a narrow consequence class.

Receipts make routing legible

The user should not have to choose a provider before delegating an outcome. Gateway can buy the cheapest sufficient intelligence inside the lane's privacy, quality, latency, and budget boundaries. Its receipt makes the actual choice and any escalation inspectable afterward.

The important economic measure is not token price in isolation. It is cost per accepted outcome at equal-or-better verified quality, including retries and human intervention.

Replay and governed learning

Recorded work can be replayed against a candidate gate, route, skill, or routine. Only evidence-backed, reviewed, reversible promotions change future behavior. The ledger therefore supports learning without allowing a model or memory store to rewrite itself silently.

Current limitation

The destination record above is not fully implemented. Run-level traces are ahead of commitment, handoff, approval, and evidence-bundle records. Real-path token spend is not yet fully instrumented end to end, and routing remains largely static. Figures presented elsewhere as modelled are not ContextStack production measurements.