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

Zaphira is the GigaSpaces Agentic Data Platform.

It protects your existing Java applications and databases from collapsing under unpredictable agentic workloads, by predicting where agent load will land and placing a purpose-built data layer between the agents and your systems, with no rewrite of the code you run today.

Existing Java applications and databases were built for paced human traffic and scheduled batch jobs.

Autonomous agents add a third pattern: continuous, parallel, self-multiplying requests that surface as database bottlenecks, ad hoc aggregation load, concurrency limits, sensitive data leaving the data layer, and high LLM token cost. Zaphira addresses each of these without changing the stack underneath.

It is a dedicated layer between external agents and your existing Java applications and databases, so for the use cases you select, agentic workflows do not call production systems directly.

It is made of Agentic Data Units (ADUs): specialized agents embedded in the data layer itself. External agents stay focused on business logic and decisions, while ADUs take over the data-intensive work, meaning efficient data access, manipulation, aggregation, and processing, and run it next to the data at in-memory speed instead of in external agent logic. This is much more than a fast cache.

An ADU is the building block of the agentic data layer: a specialized data agent embedded in the data layer itself, as opposed to an external agent that carries out a business task.

It holds the data and the business logic that operates on it in one in-memory boundary, takes over data access, manipulation, and processing for the external agents, and calls an LLM only when a step needs judgment. It exposes a native agent-to-agent (A2A) interface so external agents can query it, trigger it, or subscribe to its changes.

Zaphira is built on GigaSpaces XAP, an in-memory data grid that has carried mission-critical enterprise workloads for decades.

Zaphira adds the agent-facing capabilities on top of it: ADUs, native A2A communication, per-operation identity chains, agent-goal ledgers, event-driven notification, and side-by-side validation tooling.

Zaphira is aimed at enterprises that run mission-critical Java applications and databases and plan to put autonomous agents in front of them.

GigaSpaces customers span banking and capital markets, insurance and healthcare, travel and transportation, automotive and manufacturing, telecom and retail, and the public sector.

Pilots usually run on small data volumes and a handful of agents, so they hide the problem.

The typical symptoms appear later: pilots that slow down once pointed at production data volumes, database reads growing faster than users or transactions, the same customer or order queried repeatedly inside one agent workflow, and duplicate writes after retries.

Yes. Human and batch traffic are both predictable and can be capacity-planned.

Agents decide at runtime how much work to do: they loop, reflect on their own answers, retry after failures, and fan out into many parallel copies of themselves, so one request can become an unknown and variable number of backend operations with no schedule.

It is the best time, because Zaphira predicts where agent load will land before a single agent goes live.

The Prediction phase maps the use cases you plan onto your own APIs and data, and shows which interfaces are missing, so problems are found on paper instead of in production.

Use Zaphira Predict, which is built for exactly this.

It builds a map of your enterprise applications from signals you already have (traces, metrics, logs, API specifications, source code, and data-source metadata), then projects your planned agentic use cases onto it step by step. The result is the predicted agentic workload on each API and data source, the expected hotspots where it exceeds what the system handles today, and the interfaces that are missing, all before any agent goes live.

Your APM tells you where the pain is; GigaSpaces tells you what to do about it, and then does it.

APM tools stop at the symptom. Our pipeline takes those same signals further, identifies which bottlenecks match proven in-memory optimization patterns, and generates the fix automatically.

A cache stores data and hands it back faster; Zaphira goes much further.

Its Agentic Data Units run code embedded with the data inside the XAP in-memory grid, so business logic, aggregations, and the reasoning of an LLM execute right where the data lives. Complex problems that would otherwise take many agent round trips to your database, such as assembling a customer view or judging a case, are solved inside the data layer at in-memory speed, with the LLM called only when a step needs judgment. Keep your cache for what it does well.

Throttling protects the backend but hurts the agent: an agent has a step or time budget it cannot renegotiate, so a rejected or delayed call often becomes a partial or stale answer.

Zaphira absorbs and serves the load instead of refusing it, so production stays protected and agent output quality does not drop.

Each one solves a narrower problem and should stay.

Replicas can lag behind current state, queues do not give fresh aggregation or one transactional view across a chain of agent steps, workflow engines assume a fixed step sequence while an agent rewrites its own plan, and sagas have no record of what a self-fixing retry already committed. Agentic workloads hit the combination of these gaps at once, and stacking the tools still leaves seams.

No. MCP standardizes how an agent connects to tools and data; it does not shape, reduce, or protect traffic.

Every agent call still lands on the same Java application and database, so the load, privacy, and cost issues remain unless a layer in between absorbs them.

Zaphira is much more than a fast in-memory store.

Its Agentic Data Units attach business logic and LLM reasoning directly to the data, so the data layer itself becomes an agent. An ADU reacts the moment relevant data changes and notifies interested agents instead of being polled, computes aggregations and derived results next to the data, and answers through a native agent-to-agent interface with a finished result instead of raw records. This keeps sensitive data inside the boundary, turns repetitive access patterns into deterministic functions that need no LLM call, and stamps every operation with the identity of the agent that made it.

Capacity is sized for a known load, and agent fan-out can arrive at any moment, driven by the model's own reasoning.

Adding hardware also does not remove the redundant repeated queries, sensitive records crossing the boundary, or token spend on mechanical lookups, and the database remains the throughput ceiling for every service above it.

You would need to build and operate a distributed in-memory data layer with ACID transactions, high-throughput aggregation, secondary indexes, and horizontal scaling, and then add embedded agents, A2A, identity chains, and validation tooling on top.

Zaphira delivers this on XAP, a platform with more than 20 years in production at the world's largest enterprises.

Zaphira's prediction does build an understanding of how your application works, but your team does not have to document it first.

It learns this automatically from signals you already have (runtime telemetry, API specifications, source code, configuration, and data-source metadata) and compares it with the curated Agentic Patterns Library of known workflow and bottleneck patterns. You review and correct a draft instead of running a long discovery project, and every finding is scored and validated by a person before any remediation is proposed.

The prediction is built from your own signals (traces, metrics, logs, API specifications, source code, and data-source metadata), and it carries confidence flags wherever signals disagree or coverage is thin.

Your team validates the map of your enterprise applications and the prediction, and can edit assumptions such as loop depth and fan-out, before anything downstream depends on them.

Zaphira minimizes this risk by design, because it is a decoupling layer between the agents and your enterprise systems.

Only agentic workload reaches Zaphira; your existing workloads remain untouched, so the risk of breaking legacy workloads is almost zero. Remediations are generated into Zaphira rather than into your applications, and each one also carries a test plan and functional equivalence validation, with activation approved manually.

Your Java code, database schemas, and operational contracts stay unchanged, and nothing is added inside your applications.

For the selected use cases, the external agent works against the Zaphira agentic data layer instead of calling your production systems directly; all other traffic continues exactly as before.

Zaphira creates a prediction of the expected agentic workload on your enterprise applications, identifies the expected hotspots, and offers remediations.

To do that it needs signals you already have: distributed traces, metrics and logs, API specifications (OpenAPI, gRPC, message topics, batch jobs), source code and configuration, and data-source metadata such as schemas and indexes. Your team then confirms the draft map of your enterprise applications and adds what a machine cannot see, such as business criticality and planned changes.

For a hotspot on an existing API, the ADU keeps an in-memory representation synchronized with the underlying database, so agents read it at microsecond latency and only changes persist to the database.

The underlying XAP grid provides full ACID transactions even under concurrent agent writes, and an agent-goal ledger records what each agent instance has already committed so retries do not re-apply actions.

The layer is built on a distributed in-memory grid that scales horizontally and vertically, so capacity grows with the size of the agent fleet and not only with the size of the dataset.

The validation phase replays the predicted workload, including peak concurrency and injected retries, against each ADU before rollout.

The platform runs entirely inside your own private cloud or on-premises infrastructure; your data never leaves your perimeter.

There is no shared environment, and for AI-powered analysis you choose: run locally hosted models that never leave your infrastructure, or connect to a third-party AI provider of your choice under your own terms. Either way, GigaSpaces never has access to your data.

Not by default. Data-intensive and deterministic processing happens inside the data layer boundary, so a risk score, a yes or no decision, a summary, or an exception list leaves the boundary instead of the underlying records.

External models receive only the minimum approved context needed for a judgment.

You choose. For AI-powered analysis you can run locally hosted models inside your own infrastructure, or connect to a third-party AI provider under your own terms.

In the data layer itself, the LLM is an external resource that an ADU calls only when a step needs judgment.

Yes. Every operation carries a full identity chain: the human or business principal, the agent instance and its policy version, the tool invoked, and the target record.

Because the ADU stamps this at the point of change, attribution does not depend on reconstructing it from logs afterwards.

No. LLMs are always served inside the Zaphira cluster, or alongside it on premises, in a fully controlled environment.

Zaphira runs inside your environment with no shared environment, so your data does not leave your perimeter and GigaSpaces has no access to it.

It can. Repetitive lookup, filter, and compare patterns become deterministic functions that run next to the data without an LLM call, so tokens are spent on real judgment instead of re-reasoning over the same access pattern on every pass.

Token savings are an additional benefit; the primary value is keeping production systems safe under agentic load.

The value case is avoiding the failure of systems the business depends on, plus the evidence to proceed with confidence.

Before rollout you get a validation scorecard, measured against the Prediction phase, covering predicted load absorbed, sensitive-data payloads outside the boundary, duplicate writes on retry, LLM token reduction, and agreement of decisions with approved behavior. A person approves it before anything touches the live path.

Start with the Zaphira prediction module. It gives immediate value on its own: you explore the predicted agentic workload on your enterprise applications, where it will land, and the expected hotspots, before any agent goes live.

Then use the full remediation power of Zaphira. The four phases are Predict, Analyze, Remediate (every risk point with ADUs in an agentic data layer), and Validate (each remedy against the predicted workload before rollout), and each produces an artifact and a human confirmation step.

None to the live path. The prediction and analysis phases read signals and specifications you already have, and the validation phase runs Zaphira alongside production with no interaction between the two paths, so the live application and its users stay untouched.

It is revised or rolled back, not promoted. Validation is designed to be falsifiable: a remedy that misses its intended functional outcome, or gets the shape right but the behavior wrong, does not ship.

Every remediation stays reversible until it has been proven.

Yes. The Prediction phase proposes candidate use cases from the Agentic Patterns Library, matched to your enterprise applications, and your team confirms the ones that fit your roadmap.

The same four phases then repeat for the next tier of use cases as agent adoption grows.

Your existing Java applications and databases stay in place, unchanged.

Zaphira is deployed alongside them and is built on XAP, so the new layer is what you add, not a replacement for what you run today.

Deploying Zaphira and getting the initial prediction can be achieved in 2 to 3 days.

Connecting Zaphira to your existing telemetry, APIs, traces, and other valuable signals is a joint effort with your team, and it is where most of that time goes.

GigaSpaces has more than 20 years of production experience running in-memory data infrastructure for the world's largest enterprises, processing billions of real-time transactions daily with no impact on systems of record.

Selected customers include Bank of America, Barclays, Morgan Stanley, UBS, and American Airlines.

Book a demo at gigaspaces.com, or contact your GigaSpaces representative.

We map the agentic patterns that matter to you onto your Java enterprise applications and show where the risk is highest, before a single agent goes live.