Your models provide the intelligence.Eigen turns it into controlled execution.
Eigen One plans every task before your models run it. Same models, same tools, same providers.
The problem is not the model. It is how the work is executed.
Companies are connecting powerful models to internal data, APIs, MCP servers and business workflows. Most of those systems still run as one open-ended loop: the model gets a task and a large set of tools, decides what to do next, calls a tool, reads the result, and repeats until it believes the work is done.
Context that only grows
Prompts, tool definitions, intermediate results and conversation history accumulate, then get sent again even after they stop being relevant.
A workflow rediscovered every step
The model is asked to work out the same process from scratch at every turn, so the same reasoning is paid for repeatedly.
Work that could run at once, running in line
Independent steps wait for each other because nothing established up front which of them were independent.
Decisions that cannot be explained
The process was improvised rather than designed, so there is no plan to point at when someone asks why the system did what it did.
Observability products tell you what happened. Observing an inefficient execution does not make it efficient, and observing an unreliable one does not make it reliable. What enterprise AI is missing is a structured execution layer.
One open loop, or one compiled plan.
Eigen sits between your applications and your existing AI stack. It does not ask you to replace your models, prompts, tools, data systems or provider contracts. It controls how those parts are used to finish a task.
The open agent loop
The model improvises the process while it works, carrying everything it has seen so far.
- Every call carries the full history
- Every tool definition, every time
- Steps run one after another
- A failure restarts the workflow
A compiled execution plan
The Eigen Task Compiler turns the objective into a governed plan before expensive execution starts.
- Each step carries only its own evidence
- Only the tools that step is permitted
- Independent steps run together
- A failed step is retried on its own
- Model step
- Deterministic code
- Validation or policy gate
Planning happens efficiently, without spending frontier-model tokens on rediscovering the same structure. Your chosen models are then assigned to the steps that actually need their intelligence.
What the plan settles before a single expensive token is spent.
This is not planning in the conversational sense. It is the compilation of a business objective into an executable, governed workflow.
Shape of the work
- What has to be accomplished
- Which steps depend on other steps
- Which steps can run in parallel
What each step may touch
- Which information the step requires
- Which capabilities and tools are permitted
- Which steps need model intelligence
Where control applies
- Which operations can run deterministically
- Which results require validation
- Where policy or human approval applies
Every step receives four things, and nothing else.
Instead of handing each model call the whole workflow history and every available tool, Eigen One gives a step exactly what it needs to produce a usable result.
A precise objective
One job, stated once, with no need to infer it from the conversation so far.
The relevant evidence
The results earlier steps produced that this step actually reads, and nothing more.
The permitted tools
Only the capabilities this step is allowed to use, so the tool schema stays small.
A defined output contract
The shape the result has to take, which is what makes validation possible before anything consequential runs.
Start with one endpoint change.
Eigen One offers two levels of integration. The first measures your current workflow. The second lets Eigen own the execution loop.
Compatibility Mode
Fastest path to a benchmarkPoint an OpenAI-compatible client at Eigen One by changing the API base URL. Eigen measures model usage, repeated context, tool-schema overhead, latency, errors and cost per completed task, and applies the optimizations that are safe without owning the whole agent loop.
client = OpenAI(
base_url="https://api.eigennetworks.com/v1",
api_key=EIGEN_API_KEY,
)- No change to your prompts
- No change to your tools
- A live measurement in days
Execution Mode
Full planned executionSubmit the task and its contract, and Eigen One owns the execution loop. It compiles the plan, selects capabilities at runtime, calls your models and tools, validates intermediate results, and returns the outcome with an execution receipt.
POST /v1/tasks
{
"objective": "...",
"tools": [ /* MCP + OpenAPI contracts */ ],
"policies": [ /* approval and disposition rules */ ],
"output": { /* required schema */ },
"models": { /* your configured route */ }
}- The complete task, not a turn
- Tool and MCP contracts
- Policies and required output schema
- Your own model route
One endpoint change buys compatibility and measurement. Full task decomposition needs the task-and-tools contract, because Eigen has to control the execution lifecycle to plan it.
Your tools stay in your environment.
A plan describes the capability a step requires without binding the workflow to one implementation. At runtime Eigen discovers the eligible tools, applies permissions and policy, picks the right one, and records the link between request, action, result and supporting evidence.
- MCP servers
- OpenAPI services
- Function-calling schemas
- Internal REST and GraphQL APIs
- Databases and retrieval systems
- Existing workflow platforms
- Human-approval systems
- 01
Through Eigen
Fastest to integrate, for tools that are already reachable from the managed service.
- 02
Through your tool gateway
Eigen calls your gateway, and your gateway keeps the credentials and the audit trail.
- 03
Through a local relay
A relay inside your environment executes the call, so the tool never has to be reachable from outside.
An anti-money-laundering investigation, compiled.
A conventional agent receives the whole alert, the customer profile, the transaction history, the policy instructions and definitions for every compliance tool, then decides what to do turn by turn while carrying all of it forward. Eigen One decomposes the investigation first.
Gather the evidence
- 01
Read and normalize the alert
- 02
Retrieve the customer KYC profile
independent - 03
Retrieve transactions for the alert period
independent - 04
Identify counterparties
Screen and aggregate
- 05
Screen counterparties against jurisdiction, sanctions and PEP sources
parallel - 06
Aggregate transactions for structuring and pass-through patterns
deterministic
Decide and record
- 07
Assess the money-laundering typology
model step - 08
Apply the bank disposition policy
validated - 09
Produce the narrative
model step - 10
Submit the approved disposition
policy gate
The frontier model still writes the conclusions that need semantic judgment and the narrative that a human will read. It is no longer responsible for rediscovering and carrying the entire process by itself.
The metric is cost per successful task.
Eigen One is not a token-reduction proxy. A cheaper execution is worth nothing if it produces a worse outcome, so the benchmark measures the outcome and the cost together.
Outcome
- Successful task completion
- Output quality
- Policy compliance
- Human intervention
Cost
- Input, output and reasoning tokens
- Model and infrastructure cost
- Number of model calls
- Number of tool calls
Behaviour
- Retry and failure rates
- End-to-end latency
- Parallelism and throughput
Every execution returns a receipt
Explainability is not reconstructed afterwards from unstructured logs. Every action belongs to a plan, so the explanation already exists.
- Objective
- The business objective as it was submitted
- Plan
- The decomposition and the dependencies between steps
- Evidence
- Which sources each step actually read
- Contributors
- Which models and tools did which work
- Controls
- Validations performed, policies applied, approvals received
- Cost
- What the execution cost, and why this outcome was selected
Every execution becomes company knowledge.
Traditional agents finish a task and discard most of what they worked out about how the work should be done. Eigen keeps the validated structure. Successful plans, tool relationships, evidence paths, policies, exceptions, approvals and outcomes become reusable, versioned procedural knowledge.
The Company Brain is not another general-purpose language model, document search system or pile of embeddings. It is a structured representation of how your work is actually performed.
- How objectives are decomposed, and which systems hold the authoritative evidence
- Which tools suit which operation, and which tasks depend on which others
- Which policies control decisions, and which exceptions have to be escalated
Every task produces an outcome today and strengthens your execution intelligence for tomorrow. The knowledge arrives as a consequence of doing useful work, so there is no knowledge-management project to finish first.
Eigen One is the immediate advantage. Eigen N is complete control.
Full AI sovereignty is hard to reach in a single migration. Asking a company to replace its models, infrastructure, APIs and governance at once buys a long transformation before any value shows up. Eigen starts at the execution layer instead, where models, tools, data, policy and business objectives already meet.
Eigen One
The dominant immediate gain
The largest available improvement from a small integration, working with the AI stack you run today.
- Runs against your current models and tools
- Eigen-managed cloud, your VPC, or on premises
- Plans and controls migrate with you
Eigen N
The complete sovereign platform
Every component required for company-controlled AI, with Eigen One as the execution intelligence at its centre.
- Model infrastructure and enterprise small language models
- Private data, tool and MCP infrastructure
- Policy, identity, approvals and governance
Eigen One does not become obsolete when a customer adopts Eigen N. Sovereignty becomes a controlled progression rather than a disruptive replacement project.
Frequently asked questions
What is Eigen One?+
Eigen One is a model-agnostic task orchestration and execution layer for enterprise AI. It sits between your applications and your existing AI stack, compiles each task into a governed execution plan, then runs that plan against your own models, tools and data.
Do we have to replace our models, or move our data?+
No. You keep your models, prompts, cloud providers, APIs, data sources and MCP servers. The intelligence still comes from the providers you already trust. What changes is the structure around execution.
How is this different from an AI gateway or an observability tool?+
A gateway gives you one endpoint and routing. An observability product tells you what happened after the fact. Eigen One decides how the work is structured before it runs, then executes and validates that plan, so reliability and explainability are produced during execution rather than reconstructed from logs.
What is the difference between Compatibility Mode and Execution Mode?+
Compatibility Mode needs one endpoint change and gives you measurement plus the optimizations that are safe without owning the agent loop. Execution Mode takes the task, the tool contracts, the policies and the required output schema, and gives you full planned execution with an execution receipt.
What is the Company Brain?+
The Company Brain is the procedural knowledge that accumulates as Eigen executes work: validated plans, tool relationships, evidence paths, policies, exceptions and outcomes, versioned and reusable. It is a structured record of how your company actually performs its work, not a document search system.
How does the path to sovereign AI work?+
Start with Eigen One in the Eigen-managed cloud, prove the impact on one workflow, then move the same execution plans and controls into your private VPC or your own data centre. Eigen N extends that execution intelligence into a complete sovereign platform, with Eigen One at its centre.
Pick one expensive workflow.We will benchmark it against your current system.
Same tasks, same models, same tools, equivalent quality and policy requirements. The comparison isolates what the execution layer is worth.