| Advantage | What it gives you |
|---|---|
| Model leverage | The best model for every task, from any vendor, under your own policies. |
| Data sovereignty | Specialized models trained on your own data, owned and kept in your environment. |
| Cost governance | Full visibility and control of AI cost, with savings that compound. |
| Agent | Volume / mo | Task shape | Verifiable | Spend / mo | SLM fit | Projected savings |
|---|---|---|---|---|---|---|
| claims-extractor | 4.1M | narrow / repeat | ✓ schema | $58K | 0.94 | −$46K/mo |
| support-summarizer | 2.7M | narrow / repeat | ✓ judge | $34K | 0.91 | −$25K/mo |
| sql-assistant | 0.9M | narrow | ✓ executes | $22K | 0.88 | −$18K/mo |
| research-planner | 0.1M | open-ended | ✗ | $12K | 0.31 | keep frontier |
| Projected fleet savings | −$89K/mo | |||||
| Where scores are used | |
|---|---|
| governance | the audit trail |
| training | the reward signal |
| promotion | the promotion decision |
Am I within budget — and is the saving real?
What is worth training next — and is what I promoted still holding?
Is the optimization loop running — and is it failing safe when it fails?
Am I within budget — and is the saving real?
What is worth training next — and is what I promoted still holding?
| Agent | frontier → proved → now | $/task | State |
|---|---|---|---|
| claims-extract v5 | 0.93 → 0.96 → 0.96 | $0.0028 | holding |
| code-review v3 | 0.91 → 0.94 → 0.89 | $0.0041 | drifting |
| deploy-agent v2 | 0.88 → 0.92 → 0.92 | $0.0035 | holding |
Is the optimization loop running — and is it failing safe when it fails?
Akka Specify is spec-driven delivery: tooling for developers and non-developers to build and test agentic systems.
Specifications carry the exit conditions the system must meet. Any change that drifts from them is blocked before it merges.
The specs live with the code, so technical and knowledge debt do not accumulate. Specify runs inside the repos, CI, and AI assistants your teams already use.
Akka Verify governs agents at runtime: it decides what an agent may do and records what it did.
Policies allow, block, or report. Evaluations grade and score every response. Guardrails act inline, sanitizers redact and mask, and HITL escalations put a person in the loop.
Every run leaves a durable evidence record you can hand to an auditor, mapped against a corpus of 190 AI regulations. It covers agents built on Akka and agents built elsewhere.
Akka SDK is the component model and runtime for agentic systems, and where resilience and scalability are guaranteed.
Agents and workflows carry behaviour, entities and views carry durable state, and endpoints, timers, and consumers connect the system to everything outside it.
State is durable by default and replayable from its event journal, so an agent that fails mid-task resumes from where it stopped.
An agent interacts with an AI model to perform a specific task. It keeps contextual history in session memory, which can be shared with other agents working toward the same goal.
It can expose function tools and call them when the model asks for them.
A model-driven component that runs as a durable process. It works on typed tasks, each with its own instructions and result schema, and the runtime drives the model through a decision loop until the task is done. Agent and task state persist as it goes, so work survives crashes and restarts.
Coordination is part of the component model: an agent can delegate to specialists, hand off to peers, or lead a team sharing a task list. The runtime exposes these as tools, so multi-agent systems are assembled from focused agents without writing orchestration code.
Workflows run long-running, multi-step business processes while you write only the domain logic. They provide durability and consistency, and can call other components and services.
A business transaction lives in one place, and the workflow either keeps it moving or rolls it back when a step fails.
Instead of storing the current state, these persist every event that led to it, written to a journal with ACID semantics.
State is rebuilt by replaying those events, which scales horizontally and isolates failures — and gives you a complete history of how the state came to be.
These persist state as a single current value, keyed by id.
Akka guarantees exactly one instance of each entity across the whole cluster, so commands are handled one at a time with no concurrency to reason about. Active state is held in memory and recovered from durable storage after a restart or rebalance.
Views let you read across many entities, or find an entity by something other than its id.
Each view is built for a specific access pattern and updates as the underlying entity state changes.
An endpoint is how a service is exposed to the outside world. HTTP endpoints accept and return JSON by default.
Lower-level APIs are available when you need full control over what data is accepted and returned.
gRPC endpoints expose a service through protobuf contracts defined in .proto files, so the service contract is explicit rather than implied.
Binary serialization and protobuf’s forward and backward compatibility make service-to-service calls fast and safe to evolve without breaking existing clients.
MCP endpoints expose a service to MCP clients — LLM desktop applications, and agents running on other services.
What you expose becomes tools the model can call on its own behalf.
Timers schedule a call to run later — useful for checking whether something completed after the fact.
They are stored by the runtime and guaranteed to run at least once: if the call fails, the timer reschedules itself until it succeeds.
Consumers read a stream of events — from an entity’s journal, from key value state changes, or from a message broker topic.
They also produce events outward, which is how an Akka service interacts with systems beyond it.
Akka Optimize is how you own your intelligence: it routes traffic to the best open-weight model and trains smaller ones on your data.
Routing sends each request to the open-weight model that handles it best. Optimize reads evaluated production traffic to find work a smaller model can take, then trains, proves, and promotes it.
The measure is cost per verified task. Quality is held to the evidence Verify already collects, so cost falls without behaviour drifting.
