Asset Retrieval
The asset the next step needs can't be found in time.
The decision
A job needs a specific container, trailer, or tool, and its last-known location is stale. Two candidates match — one closer but uncertain — and the job starts in 40 minutes. Send a retriever to the likely one, search, or stage a substitute?
Why it's hard today
Chase the wrong location and you burn the window; wait for certainty and you're late. Asset positions are probabilistic — last-seen, not live — and confidence varies by zone and tag.
Your tracking system gives a last-known position with a confidence you can't see; it doesn't decide whether that confidence is good enough to act on, or what to do if it isn't. It shows a dot, not a retrieval plan.
What AgentForgeOS does
It assembles the picture you can't see, and makes the call under your rules.
Dispatch to the higher-confidence location while staging the substitute as a hedge — making the window either way — under your retrieval policy.
- Last-known position and confidence
- Candidate matches and proximity
- Retriever availability
- Job start window
- Substitute options
- Confidence thresholds for action
- Window-protection rules
- Retriever-allocation limits
- Substitute-fallback rules
⚠ Asset needed in 40 min — A-3092, location confidence medium
Evidence assembled
- Last seen zone 4, 18 min ago (medium confidence)
- Second candidate in zone 7, low confidence
- One retriever free
- Substitute available but slower
Recommendation · dispatch to zone 4, stage substitute as backup
Policy: within window · retriever available · awaiting yard lead
What improves
- Fewer missed job windows
- Less time burned chasing stale positions
- Hedged against bad reads
Each retrieval and whether the asset was where expected is kept, so the model learns which zones and tags to trust, by hour and condition.
Underneath, this is the same operating model the rest of the platform runs: verified context is assembled, options are weighed and adversarially challenged, the decision is governed by your policy, and the outcome is learned. The decision changes from one of these to the next. The architecture does not.
See how it worksThis is exactly how your team works today.
Only now the decision is assembled, governed, and remembered — instead of made from memory and lost.