Your Mission-Critical Java Applications Were Built for People. Not AI Agents.

Their own queries get slower with every pass, timeouts trigger retries that add still more load, and a reasoning loop stalls waiting on a resource that keeps degrading. Accuracy and consistency risk: a slow or stalled query can force the agent to proceed on a partial or cached result instead of the full one.

The database becomes the throughput ceiling for the whole application layer sitting above it. Response times climb, connection pools saturate, and every existing service that depends on that database slows down or times out together, including ones with no relation to agents. Concurrent agents writing to the same row, such as several agents updating the same order or ticket at once, add row-level lock contention and write amplification, producing lock-wait timeouts and deadlocks that a human-paced workload would rarely trigger.

A support-ticket triage agent re-checks the same customer's order history at every reasoning step. Multiply that across hundreds of tickets processed in parallel, and a database that used to comfortably serve a few hundred people now faces thousands of near-simultaneous, repeating queries.

For operational rollups, an agent either waits on a query the OLTP system wasn't tuned to serve on demand, or falls back to computing the rollup itself inside its own reasoning, burning extra steps and time. For analytical requests, an agent that needs a fresh aggregate either waits on a scheduled report that hasn't run yet or gets an answer computed against last night's load. Accuracy and consistency risk: without an on-demand aggregate, the agent may act on stale or self-computed numbers instead of current ones.

Operational systems absorb a new class of on-demand rollup queries they weren't indexed for, while BI systems and data warehouses tuned for scheduled, pre-optimized queries receive a stream of ad hoc aggregation requests from many agents at once, straining query planners and ETL pipelines that were never designed to serve that pattern live. Some of these requests combine structured aggregation with vector similarity search, a hybrid query shape that neither system's existing indexes were built to answer together.

A support agent asks its OLTP-backed order service for a customer's real-time order-and-refund summary, a query shape the schema supports but was never indexed for repeated on-demand use. Separately, a coordinating fraud-review agent asks the central data warehouse for a cross-customer anomaly trend, while dozens of other agents ask for their own unrelated ad hoc aggregations against the same two systems at the same time.

Agents queued behind an exhausted pool stall mid-task, a step's timeout or stopping condition can fire before a tool call ever returns, and autoscaling tuned for gradual, predictable growth can't react fast enough to a spike that arrives with no advance warning. Accuracy and consistency risk: an agent delayed by pool exhaustion can hit its own step timeout and proceed with a partial or missing result instead of the completed call.

Application-tier and data-tier capacity, connection pools, thread pools, and provisioned infrastructure, exhaust or saturate with no warning, and the resulting failures spill over onto ordinary human users sharing the same pool, not just the agent swarm that caused it.

At 3 a.m., a single customer request triggers an orchestrator that spins up 50 parallel research agents. The application's connection pool, sized for 20 concurrent human sessions during business hours, runs out within seconds, so every other user of the service starts seeing connection timeouts while the queued or rejected agent requests stall mid-task.

Agents end up handling and transmitting PII, financial data, or health records they were never actually asked to expose, widening the blast radius of any prompt injection, logging misconfiguration, or model-provider data-retention policy the agent's pipeline touches.

Every heavy agentic analysis becomes a bulk data-extraction event by default. Compliance and data-residency controls built around the data layer's own boundary, encryption at rest, access control, audit logging, are bypassed the moment the raw records leave that boundary for external processing, even though nothing outside the data layer needed to see them.

A fraud-review agent is asked whether a customer's last 200 transactions look anomalous. To answer, it pulls all 200 full transaction records, including counterparties, amounts, and account numbers, into its own context to look for patterns, then reports back a single risk score.

The larger and more frequent the data an agent has to pull into its context to reason over, the more input and output tokens each step consumes, and the cost compounds across every record, every retry, and every reasoning step in a long-running task.

Heavy, data-intensive agent workloads translate directly into LLM inference spend that scales with data volume and query frequency rather than with the value of the final decision, an efficiency gap that widens as agent adoption and data volumes grow.

An agent triaging expense reports pulls a claimant's last twelve months of transactions and asks the LLM to find any that duplicate the current claim, spending a full token-priced reasoning pass on a comparison that is deterministic and identical in shape every time.