What happens between a contract arriving and a lawyer seeing it — extraction, category routing, parallel scoring, and the escalation rules that decide which clauses a person must read.
A contract review system that reads your own playbook is four services and three queues. This is the whole pipeline — what each stage costs, where it degrades, and the one design decision the architecture is built around.
Ordered by chapter. Each post stands alone but builds on the one before it.
A contract-review pipeline scores every chunk across twelve legal categories and routes it to eleven specialists. The missing twelfth is not an oversight — it is the control signal the whole cost model depends on.
Parallel specialist agents all read the same text, so they all report it. Deduplicating across agents is not tidying — it is what decides whether the output is usable, and it is a different problem from deduplicating messages.
A focused application of the LLMOps routing pattern to legal contract analysis — the analyst-selection logic that ships fewer clauses to fewer agents and finishes a 3,300-call review in 154 seconds.
A queue worker can retry in-process or let the queue redeliver. Doing both is correct — and the two tiers multiply rather than add, which is the cost nobody budgets for.
Retry is how a degraded model provider becomes an outage. A circuit breaker fails fast instead — and in a container fleet it protects far less than you think, because every replica has its own.
When three of twelve agents fail, does the job fail? Making that a configured threshold rather than an exception is the right call at scale — and it produces results that carry no record of how complete they are.
The same pipeline ingests a spreadsheet with no model calls and a PDF with dozens. The format your customer sends decides your unit economics — which makes the upload form a cost lever most teams never touch.
Amazon ECS documents task scale-in protection and names queue-processing workloads in its first example. It works — and it will block your next deployment in ways AWS spells out and most teams discover during a release.
Hot-reloading agent prompts and thresholds from a parameter store is operationally excellent and a governance problem — a behaviour change with no diff, no review and no rollback story.
Putting AI review inside Word rather than in a portal is a distribution decision, and it cascades — into how you authenticate, what your output can be, and who owns the surface you ship on.
Five decisions an AI contract reviewer does not make, each bounded to what we actually built — including the one that matters most, which is that nothing in the system ever questions the playbook it was given.