Slow enterprise systems, made fast. AI agents that show receipts.

Large companies must continuously prove that no employee holds conflicting permissions. For example, the ability to both create and approve the same payment. For one large enterprise production workload, this check took about 20 hours at 65 GB because the old system re-examined the organization hierarchy separately for every rule. I rebuilt the engine to resolve the hierarchy once and compare permissions with bit-level operations. The redesigned path runs in a few minutes under about 1 GB, with matching violations across the rollout comparisons. Worst-case production scope, not a lab benchmark.

Audit and compliance teams periodically need complete data exports as formatted Excel workbooks in S3. The previous exporter assembled entire workbooks in memory and crashed on large reports. I built s3-outputstream, an Apache-2.0 Java OutputStream over S3 multipart with one reusable 5 MiB part buffer: an upload publishes only when the producer declares success, otherwise it is discarded. v2.0.0 is on Maven Central with a real-S3 conformance receipt. Bounded application buffering, not bounded total process memory — producer, SDK and per-part overhead are separate budgets.

Companies now build AI assistants that hold tools, data access, and login credentials. These assistants can call other assistants or share credentials with them. The dangerous permission combinations we police in humans quietly reappear, with no person in the loop. I formulated effective access for agents across five patterns — a single over-privileged agent, conflicts spanning agent and owner, delegation chains, credential-sharing groups, actions on a user's behalf — and built a deterministic evaluation engine where every finding cites its evidence and is tracked to resolution. Latency and deployment scope stay out until measured and cleared.