Traceability

Autonomous work can involve many decisions, agents, tools, handoffs, actions, and pieces of evidence.

If those parts cannot be connected afterwards, the system may know that something happened without being able to explain how one state led to another.

That is the role of Traceability.

Traceability preserves the meaningful links across governed work so AG Arch can reconstruct:

• where the work started;
• which mandate governed it;
• which capabilities handled it;
• what actions occurred;
• what evidence was produced;
• how the final verdict was reached;
• what happened when the work changed direction.

The purpose is not to record every thought produced by an AI model.

It is to preserve the operational history needed to understand and prove the work.

Traceability connects the lifecycle

Each lifecycle stage produces information that matters later.

For example:

Intent & Reality establishes the starting point.

Define Success establishes the criteria.

Plan & Govern establishes the permitted path.

Handoff & Route establishes which capability received the work.

Execute produces actions and state transitions.

Observe & Prove produces observations and evidence.

Verified Outcome records the resulting verdict.

Traceability connects those stages into one continuous chain.

Without that connection, they become isolated records.

With it, AG Arch can answer:

Why did this action happen, and how does it relate to the result being claimed?

Execution state shows where the work is

Autonomous tasks may last longer than one model call or one tool invocation.

They may be paused, handed off, retried, replanned, or resumed later.

AG Arch therefore needs to preserve meaningful execution state.

That might include whether the work is:

• being planned;
• waiting for a required condition;
• being executed;
• handed to another capability;
• collecting evidence;
• recovering from a problem;
• completed.

Execution state helps the system understand what should happen next rather than reconstructing the entire task from scratch every time.

Receipts record important events

Some actions or transitions should leave a durable record that they occurred.

We can think of those records as receipts.

A receipt might establish that:

• a capability was selected;
• an action was attempted;
• a validation ran;
• a handoff occurred;
• evidence was collected;
• a particular condition blocked execution.

The receipt is not automatically proof that the whole task succeeded.

It proves something narrower:

This event or transition occurred.

Several such receipts may later contribute to the evidence used to prove the larger outcome.

Lineage connects one step to another

In multi-step autonomous work, it is not enough to know that several artifacts exist.

AG Arch also needs to know how they are related.

That relationship is lineage.

For example:

intent → plan → handoff → action → observation → evidence → verdict

If another specialist takes over partway through the task, the new work should still be connected to the original governed task.

If execution is retried, the new attempt should be distinguishable from the previous one.

If evidence is collected after a particular action, AG Arch should be able to identify which action produced the state being observed.

Current AG Arch implementation already uses lineage in places where proof must remain connected across lifecycle transitions, including after repository changes such as PR-to-merge transitions.

Provenance tells us where information came from

Lineage explains relationships between parts of the work.

Provenance explains the origin of information or evidence.

For example:

• Which system produced this observation?
• Which execution produced this receipt?
• Which run produced this evidence?
• Which capability generated this artifact?
• Was this fact observed at runtime or inferred from another source?

This matters because two pieces of information can look similar while having very different evidential value.

A runtime observation is not the same as an agent's assumption.

A real execution receipt is not the same as a simulated result.

AG Arch already uses provenance distinctions in its internal evidence machinery, including cases where only runtime-backed records are accepted as evidence.

History makes autonomous work inspectable over time

Traceability is not useful only while a task is running.

It also creates a usable history of governed work.

That history can help answer later questions such as:

• Why was this change made?
• Which mandate allowed it?
• Which system state existed at the time?
• Which capability performed it?
• What evidence supported completion?
• Was there an earlier failed attempt?
• Why was the work replanned or rerouted?

This becomes especially important when autonomous systems perform many tasks over long periods.

Without history, every investigation starts from fragments.

With traceability, previous work becomes understandable rather than merely logged.

Traceability does not mean storing everything

AG Arch does not need to capture every internal token, intermediate thought, or irrelevant system event.

That would create enormous amounts of data without necessarily improving accountability.

Useful traceability focuses on information that matters to governed execution:

• state transitions;
• mandate and scope;
• capability selection;
• significant actions;
• handoffs;
• observations;
• evidence;
• verdicts;
• recovery decisions.

The goal is not maximum logging.

The goal is enough structured history to explain and prove what happened.

Traceability must survive handoffs

A common problem in multi-agent systems is that context becomes disconnected when one agent hands work to another.

Agent A knows why the task exists.

Agent B receives only a shortened instruction.

Agent C later receives an output from B.

Eventually nobody can reconstruct the original reason for the action.

AG Arch is designed to avoid that pattern.

The important identifiers and relationships should continue through handoffs so the work remains connected to its original intent and mandate.

This allows specialists to change without breaking the history of the task.

Traceability also supports recovery

Traceability becomes particularly valuable when something goes wrong.

Suppose the result is NOT PROVEN.

The system needs to understand where the problem occurred.

Was:

• the original plan wrong?
• required authority missing?
• the wrong capability selected?
• execution unsuccessful?
• reality different from the assumptions?
• evidence missing?

A traceable history allows AG Arch to return to the appropriate part of the lifecycle instead of restarting blindly.

This is what makes structured recovery possible.

Example — changing a system configuration

Continuing the same example:

AG Arch receives the intent to correct a service configuration.

Traceability connects:

1. the original intent; 2. the initial configuration state; 3. the success criteria; 4. the governed mandate; 5. the capability selected to perform the change; 6. the configuration action; 7. the resulting runtime observation; 8. the service-health evidence; 9. the final verdict.

Suppose the first execution attempt changes the configuration source but the running service continues using the old value.

The result is NOT PROVEN.

A second action reloads the service.

Observation now shows the approved configuration is active and the service is healthy.

Traceability preserves both attempts.

The final result does not simply say:

“Configuration fixed.”

It remains possible to understand that:

• the first action changed the source but did not produce the full outcome;
• the evidence exposed that difference;
• the system continued through a governed recovery path;
• the later action produced the state that was finally proven.

How Traceability supports AG Arch

Traceability provides continuity across autonomous work through:

Execution State · Receipts · Lineage · Provenance · History

Together they allow AG Arch to connect decisions, actions, evidence, and outcomes without depending on an agent's memory or final narrative.

The lifecycle describes how governed work moves forward.

The supporting systems make that lifecycle possible.

But autonomous work will not always follow the ideal path.

Sometimes the plan is wrong, authority is insufficient, the selected capability cannot complete the task, or the evidence does not prove the result.

The next part explains what AG Arch does then: