Deterministic Decision Layer for Agentic Systems

Client Overview

Our client is an independent systems architect exploring the infrastructure layer above agent execution frameworks. With a background in protocol design and enterprise systems, they identified a gap in how multi-agent systems make coordination decisions and wanted a rigorous reference implementation to demonstrate what that layer could look like.

Deterministic Decision Layer for Agentic Systems — Genta AI case study
Deterministic Decision Layer for Agentic Systems — Genta AI case study

The Challenge

01

As multi-agent systems scale, execution becomes cheap but coordination becomes the bottleneck. There was no standard way to answer: which tasks should run, in what order, and with how much budget? Most frameworks run everything and hope for the best. They needed a clean, structured decision interface — one that was fully deterministic, explainable, and configurable without any ML or probabilistic inference, so it could serve as a credible protocol reference rather than just another AI tool.

The Challenge

01

As multi-agent systems scale, execution becomes cheap but coordination becomes the bottleneck. There was no standard way to answer: which tasks should run, in what order, and with how much budget? Most frameworks run everything and hope for the best. They needed a clean, structured decision interface — one that was fully deterministic, explainable, and configurable without any ML or probabilistic inference, so it could serve as a credible protocol reference rather than just another AI tool.

The Solution

02

Genta built the decision layer: a stateless, config-driven decision API that sits above any agent execution framework. The system exposes three endpoints: /evaluate scores a single task and returns a decision with a full reasoning trail, /prioritize ranks a list of competing tasks deterministically, and /allocate distributes a budget pool using a greedy priority strategy. All scoring logic is driven by a single YAML config file — weights, thresholds, and reason code rules are all editable without touching code. Every output includes structured reason codes derived from explicit thresholds, making every decision fully auditable. The system was deployed on Railway and delivered within two weeks.

The Solution

02

Genta built the decision layer: a stateless, config-driven decision API that sits above any agent execution framework. The system exposes three endpoints: /evaluate scores a single task and returns a decision with a full reasoning trail, /prioritize ranks a list of competing tasks deterministically, and /allocate distributes a budget pool using a greedy priority strategy. All scoring logic is driven by a single YAML config file — weights, thresholds, and reason code rules are all editable without touching code. Every output includes structured reason codes derived from explicit thresholds, making every decision fully auditable. The system was deployed on Railway and delivered within two weeks.

Technologies Used

03

• Python • FastAPI • Pydantic • PyYAML • Uvicorn • pytest • Railway (deployment)

Technologies Used

03

• Python • FastAPI • Pydantic • PyYAML • Uvicorn • pytest • Railway (deployment)

The Results

The Results

The Results

Delivered a production-grade reference implementation in two weeks — on spec, on budget, and fully documented. The system handles all three coordination decisions deterministically, with zero ML dependencies and complete explainability on every output.

3

3

3

Endpoints Delivered

2 weeks

2 weeks

2 weeks

From Spec to Live Deployment

100%

100%

100%

Deterministic — Every Decision Reproducible

Tell us where the manual work hurts

We’ll tell you straight whether AI can fix it, what it costs, and what it should return. Whatever we build, you own.

Let's Connect

Tell us where the manual work hurts

We’ll tell you straight whether AI can fix it, what it costs, and what it should return. Whatever we build, you own.

Let's Connect

Tell us where the manual work hurts

We’ll tell you straight whether AI can fix it, what it costs, and what it should return. Whatever we build, you own.

Let's Connect

Deterministic Decision Layer for Agentic Systems

Client Overview

Our client is an independent systems architect exploring the infrastructure layer above agent execution frameworks. With a background in protocol design and enterprise systems, they identified a gap in how multi-agent systems make coordination decisions and wanted a rigorous reference implementation to demonstrate what that layer could look like.

Deterministic Decision Layer for Agentic Systems — Genta AI case study
Deterministic Decision Layer for Agentic Systems — Genta AI case study

The Challenge

01

As multi-agent systems scale, execution becomes cheap but coordination becomes the bottleneck. There was no standard way to answer: which tasks should run, in what order, and with how much budget? Most frameworks run everything and hope for the best. They needed a clean, structured decision interface — one that was fully deterministic, explainable, and configurable without any ML or probabilistic inference, so it could serve as a credible protocol reference rather than just another AI tool.

The Challenge

01

As multi-agent systems scale, execution becomes cheap but coordination becomes the bottleneck. There was no standard way to answer: which tasks should run, in what order, and with how much budget? Most frameworks run everything and hope for the best. They needed a clean, structured decision interface — one that was fully deterministic, explainable, and configurable without any ML or probabilistic inference, so it could serve as a credible protocol reference rather than just another AI tool.

The Solution

02

Genta built the decision layer: a stateless, config-driven decision API that sits above any agent execution framework. The system exposes three endpoints: /evaluate scores a single task and returns a decision with a full reasoning trail, /prioritize ranks a list of competing tasks deterministically, and /allocate distributes a budget pool using a greedy priority strategy. All scoring logic is driven by a single YAML config file — weights, thresholds, and reason code rules are all editable without touching code. Every output includes structured reason codes derived from explicit thresholds, making every decision fully auditable. The system was deployed on Railway and delivered within two weeks.

The Solution

02

Genta built the decision layer: a stateless, config-driven decision API that sits above any agent execution framework. The system exposes three endpoints: /evaluate scores a single task and returns a decision with a full reasoning trail, /prioritize ranks a list of competing tasks deterministically, and /allocate distributes a budget pool using a greedy priority strategy. All scoring logic is driven by a single YAML config file — weights, thresholds, and reason code rules are all editable without touching code. Every output includes structured reason codes derived from explicit thresholds, making every decision fully auditable. The system was deployed on Railway and delivered within two weeks.

Technologies Used

03

• Python • FastAPI • Pydantic • PyYAML • Uvicorn • pytest • Railway (deployment)

Technologies Used

03

• Python • FastAPI • Pydantic • PyYAML • Uvicorn • pytest • Railway (deployment)

The Results

The Results

The Results

Delivered a production-grade reference implementation in two weeks — on spec, on budget, and fully documented. The system handles all three coordination decisions deterministically, with zero ML dependencies and complete explainability on every output.

3

3

3

Endpoints Delivered

2 weeks

2 weeks

2 weeks

From Spec to Live Deployment

100%

100%

100%

Deterministic — Every Decision Reproducible

Tell us where the manual work hurts

We’ll tell you straight whether AI can fix it, what it costs, and what it should return. Whatever we build, you own.

Let's Connect

Tell us where the manual work hurts

We’ll tell you straight whether AI can fix it, what it costs, and what it should return. Whatever we build, you own.

Let's Connect

Tell us where the manual work hurts

We’ll tell you straight whether AI can fix it, what it costs, and what it should return. Whatever we build, you own.

Let's Connect

Deterministic Decision Layer for Agentic Systems

Client Overview

Our client is an independent systems architect exploring the infrastructure layer above agent execution frameworks. With a background in protocol design and enterprise systems, they identified a gap in how multi-agent systems make coordination decisions and wanted a rigorous reference implementation to demonstrate what that layer could look like.

Deterministic Decision Layer for Agentic Systems — Genta AI case study
Deterministic Decision Layer for Agentic Systems — Genta AI case study

The Challenge

01

As multi-agent systems scale, execution becomes cheap but coordination becomes the bottleneck. There was no standard way to answer: which tasks should run, in what order, and with how much budget? Most frameworks run everything and hope for the best. They needed a clean, structured decision interface — one that was fully deterministic, explainable, and configurable without any ML or probabilistic inference, so it could serve as a credible protocol reference rather than just another AI tool.

The Challenge

01

As multi-agent systems scale, execution becomes cheap but coordination becomes the bottleneck. There was no standard way to answer: which tasks should run, in what order, and with how much budget? Most frameworks run everything and hope for the best. They needed a clean, structured decision interface — one that was fully deterministic, explainable, and configurable without any ML or probabilistic inference, so it could serve as a credible protocol reference rather than just another AI tool.

The Solution

02

Genta built the decision layer: a stateless, config-driven decision API that sits above any agent execution framework. The system exposes three endpoints: /evaluate scores a single task and returns a decision with a full reasoning trail, /prioritize ranks a list of competing tasks deterministically, and /allocate distributes a budget pool using a greedy priority strategy. All scoring logic is driven by a single YAML config file — weights, thresholds, and reason code rules are all editable without touching code. Every output includes structured reason codes derived from explicit thresholds, making every decision fully auditable. The system was deployed on Railway and delivered within two weeks.

The Solution

02

Genta built the decision layer: a stateless, config-driven decision API that sits above any agent execution framework. The system exposes three endpoints: /evaluate scores a single task and returns a decision with a full reasoning trail, /prioritize ranks a list of competing tasks deterministically, and /allocate distributes a budget pool using a greedy priority strategy. All scoring logic is driven by a single YAML config file — weights, thresholds, and reason code rules are all editable without touching code. Every output includes structured reason codes derived from explicit thresholds, making every decision fully auditable. The system was deployed on Railway and delivered within two weeks.

Technologies Used

03

• Python • FastAPI • Pydantic • PyYAML • Uvicorn • pytest • Railway (deployment)

Technologies Used

03

• Python • FastAPI • Pydantic • PyYAML • Uvicorn • pytest • Railway (deployment)

The Results

The Results

The Results

Delivered a production-grade reference implementation in two weeks — on spec, on budget, and fully documented. The system handles all three coordination decisions deterministically, with zero ML dependencies and complete explainability on every output.

3

3

3

Endpoints Delivered

2 weeks

2 weeks

2 weeks

From Spec to Live Deployment

100%

100%

100%

Deterministic — Every Decision Reproducible

Tell us where the manual work hurts

We’ll tell you straight whether AI can fix it, what it costs, and what it should return. Whatever we build, you own.

Let's Connect

Tell us where the manual work hurts

We’ll tell you straight whether AI can fix it, what it costs, and what it should return. Whatever we build, you own.

Let's Connect

Tell us where the manual work hurts

We’ll tell you straight whether AI can fix it, what it costs, and what it should return. Whatever we build, you own.

Let's Connect