Resource Assignment
The right machine, bay, or crew for the job isn't obvious.
The decision
A job is ready, but the ideal resource is busy, a second is available but slower, and a third needs a changeover. Wait for the ideal, take the available, or change over the third?
Why it's hard today
Assign to the wrong resource and you lose throughput or quality; wait and you idle the job. The call depends on capability, availability, changeover, and the job's priority — rarely visible together.
Your scheduler assigns by rule or availability; it doesn't weigh capability against changeover against priority. It picks a slot, not the best fit.
What AgentForgeOS does
It assembles the picture you can't see, and makes the call under your rules.
Change over the capable third resource and start now — beating the due date without the yield hit — under your assignment policy.
- Resource capability and status
- Changeover requirements
- Job priority and due date
- Quality / capability match
- Crew skills
- Capability-match rules
- Changeover-cost limits
- Due-date protection
- Crew-skill constraints
⚠ Resource contention — J-2208 ready, ideal machine busy
Evidence assembled
- Ideal machine free in 90 min
- Available machine slower, lower yield
- Third needs a 30-min changeover
- Job due in 3 hours
Recommendation · change over the third, assign now
Policy: capability match ok · changeover within limit · due-date met · awaiting supervisor
What improves
- Higher throughput and yield
- Fewer idle jobs
- Resources matched to the work
Each assignment and how it ran is kept, so the model learns each resource's real capability and changeover economics.
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.