Zaphira: Protects enterprise systems for agentic workloads

Value: See exactly where tomorrow's agent load will land and where the gaps are before a single agent goes live.

Phase 1 combines two workstreams: discovery of what your Java estate actually looks like today, and use-case selection from a library of agentic workflow patterns. The Agentic Workload Prediction is the intersection of the two.

Discover what the estate actually is

Zaphira reconstructs an estate map from signals you already have:

• Distributed traces reveal service topology and the call graph between capabilities.
• Metrics and logs show current load, latency, and data-access patterns under human and batch traffic.
• API specifications, including OpenAPI, gRPC protobufs, message topics, and batch jobs, inventory the interfaces agents can reach.
• Source code and configuration identify capabilities and interfaces that may be dormant during observation but are still part of the system.
• Data-source metadata, including schemas, indexes, and connection configurations, shows which structures sit behind each interface and whether they suit the query patterns agents will generate.

The output is an estate map of capabilities, interfaces, data sources, and their relationships, with confidence flags wherever source signals disagree or coverage is thin. Your team validates the draft, corrects misattributions, and adds business criticality, planned changes, or external dependencies the automated pass cannot see.

Select the use case

In parallel, Zaphira starts from a curated library of agentic workflow patterns such as support-ticket triage, fraud review, refund handling, claims processing, and research or diagnosis swarms. Each pattern carries a characteristic workflow shape: typical loop depth, reflection or judge cycles, expected fan-out, and requirements for durable state or shared context.

Zaphira matches that library against the validated estate map and your business context to propose relevant use cases. Your team confirms the use case that matches the roadmap, refines its assumptions, and adds anything the library missed.

Generate the Agentic Workload Prediction

Zaphira projects the selected use case step by step onto the discovered estate. At each workflow step, an LLM-driven reasoning pass identifies which existing interface can satisfy it, which data sources sit behind that interface, and how loops, reflection, retries, and fan-out amplify the volume at that touchpoint. When the workflow needs something the estate does not provide, Zaphira records a gap rather than silently completing it.

The resulting prediction shows:

• Existing services and APIs the use case will call, in what sequence, and at what expected volume.
• Data sources the use case will read and write, including repeated interactions caused by reasoning loops.
• Missing interfaces, such as an aggregated view, shared-context surface, or agent-facing capability the application does not expose today.
• Direct database exposure, where agent traffic would reach a database without an API or governance boundary in front of it.

The Agentic Workload Prediction is delivered as a concrete, confidence-flagged artifact. Stakeholders validate the mapped workflow and amplification assumptions before Phase 2 begins, so nothing downstream depends on an unvalidated prediction.

Value: Get a prioritized picture of tomorrow's failure points, hotspots, missing APIs, and unshielded data paths, grounded in your own telemetry and code.

Phase 2 analyzes the validated Agentic Workload Prediction together with what already exists. The prediction says where future load will go. Live traces, metrics, and logs show how the system behaves today, while source code and configuration reveal schemas, indexes, connection pools, and implementation constraints. Cross-referencing them exposes where predicted demand and current capacity will collide.

Hotspots on existing surfaces

These are APIs, services, or data paths that already exist but will come under pressure from the predicted workload. For example, a support-triage agent that rechecks the same order history at every reasoning step can turn an API that comfortably serves hundreds of people into one facing thousands of near-simultaneous repeated queries. Telemetry shows how close the interface already runs to its ceiling; the prediction shows how much the selected use case will amplify it.

Missing surfaces

These are capabilities the selected use case needs but the application does not cleanly expose. The interface may be absent altogether, such as a shared-context service, an on-demand aggregation, or an agent-friendly view over fragmented data. In other cases the agent would reach a database table directly because no service boundary exists where governance, throttling, or shielding can live. Both are carried forward as explicit gaps.

Prioritized, evidence-backed findings

Every finding is tied to a specific workflow step, interface, capability, or data-access point and mapped, where applicable, to one or more of the five published problems:

• Database bottlenecks caused by repeated, amplified reads and writes under agent load.
• Ad hoc real-time aggregation that strains operational systems and BI platforms.
• Concurrency and capacity pressure when workloads designed for people face agent swarms.
• Sensitive-data exposure when raw records leave the data boundary for agentic processing.
• High LLM token cost when models repeatedly process raw database results and mechanical data-access operations.

Missing-surface findings do not always map neatly to one problem, and they are retained on their own merit. Each finding explains what it is, why it matters in customer terms, and what kind of change it requires. The output is a defensible, prioritized picture of where the stack will strain and where capability is missing before any remediation is built.

Value: A purpose-built agentic data layer that shields every hotspot and gap without changing a line of your existing application code.

Remediation is where Zaphira lands. For the use case selected in Phase 1, it becomes the data plane agents use when they need data, state, aggregation, or coordination. Existing Java applications and databases stay in place; agents no longer touch them directly for that flow.

The layer delivers four kinds of value at once:

• Speed. Reads, state, and coordination happen at in-memory latency next to the data.
• Data security by design. Sensitive records remain inside the governed data boundary; only the decision-ready result or the minimum approved context leaves it.
• Lower LLM token cost. Deterministic work such as fetching, filtering, joining, and validating runs as code, so the model is invoked only for judgment that needs it.
• Control at swarm scale. Every operation carries a full identity chain, making per-agent attribution native to the layer rather than something reconstructed afterward.

Agentic Data Units: business logic that lives with the data

An Agentic Data Unit (ADU) is a specialized, autonomous data agent that brings grid-resident data, the business logic that operates on it, and durable intermediate state into one in-memory boundary. LLM reasoning remains an external resource that the ADU invokes only when a judgment step needs a model. Through a native agent-to-agent (A2A) interface, the ADU becomes an active participant that external agents can query, trigger, subscribe to, or delegate work to, rather than a passive store they merely read.

Six properties make ADUs the right building blocks for the risks Zaphira identifies:

• Colocated reasoning. Business logic runs at in-memory, microsecond speed next to the data, instead of the application layer making a network call to a separate database for every step of an agent loop.
• Event-driven, not polled. ADUs react the instant a relevant object changes and can proactively notify agents and clients, removing the polling and redundant re-querying that agent loops otherwise generate.
• Native agent-to-agent (A2A) communication. ADUs expose bi-directional A2A channels, giving cooperating agents a live, shared, queryable surface instead of requiring every agent to pass full context payloads to every other agent.
• Privacy by containment. Data-intensive and deterministic processing happens inside the data layer boundary. Raw sensitive records remain governed there, while external models receive only the minimum approved context needed for a judgment.
• Lower token cost via deterministic functions. Repetitive data-access patterns become deterministic functions attached to the data, cutting how much agentic interaction needs an LLM call at all.
• Per-operation identity. Every operation carries the human or business principal, agent instance and policy version, tool invoked, and target record, so attribution is captured at the point of change.

An ADU for every finding

For each Phase 2 finding, the remediation agent shapes an ADU around the specific hotspot or gap:

• An existing API hotspot receives a synchronized in-memory representation so repeated agent reads no longer make the database the throughput ceiling.
• A missing API becomes a governed A2A interface over colocated data and deterministic business logic, such as a current customer view assembled from several tables.
• A direct database call is mediated by an ADU so governance, throttling, idempotency, and audit have an explicit boundary.
• Sensitive-data processing is reduced to governed in-boundary operations, returning a score, flag, summary, or exception list instead of exposing the underlying records.
• Repetitive database interactions become deterministic functions, reserving LLM tokens for the final judgment rather than repeated lookup-and-compare work.
• A shared-context gap becomes an active workspace that cooperating agents can query, update, and subscribe to over A2A.
• A durable state gap becomes grid-resident state that survives service boundaries and lets a failed workflow resume without restarting from step one.
• A retry and consistency gap receives an agent-goal ledger that records what a specific agent instance has already committed and integrates with existing saga tooling.
• An identity and audit gap is closed by stamping the full identity chain at the point of each change.

Nothing in the existing application changes: Java code, database schemas, and operational contracts remain in place while the ADU sits between the agent and that stack. Designs are proposed automatically for each finding from its problem class and shape, then confirmation-gated before any deployment change executes. Every remedy remains reversible if Phase 4 shows that it does not behave as predicted.

Value: Roll out with evidence, not hope: functional proof that the data layer behaves as the Agentic Workload Prediction required before anything touches the live path.

Every Phase 3 remedy was designed against a specific finding. Phase 4 verifies, on the customer's side, that each ADU behaves the way that finding demands before it is promoted.

• Side-by-side deployment. Zaphira runs alongside the existing production system while the live application path remains unchanged.
• Functional validation. Each ADU is exercised through the exact queries, aggregations, state transitions, containment boundaries, and coordination patterns described by the Agentic Workload Prediction.
• Measurable targets. The scorecard verifies that intended traffic reaches the ADU instead of the database, state survives across steps, cross-agent coordination remains consistent, sensitive data stays contained, and deterministic work is moved off the LLM.
• Behavioral scoring. An LLM-as-a-judge checks whether outcomes are correct, current, and complete. Fast or inexpensive but stale is not success; correct, current, and complete is.
• Human approval. A person must approve both the measurements and the behavioral score before anything from the new layer touches the live path.

Validation is deliberately falsifiable. A remedy that does not deliver its intended functional outcome, or produces the right shape but the wrong behavior, is revised or rolled back rather than promoted on faith.

After rollout, actual usage replaces assumptions, new use cases emerge, and the estate changes. Zaphira reruns the same four phases for the next tier of use cases, turning agent readiness into a standing capability rather than a one-time project.