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Back[ Coventa ]Case Study

[ APPLICATIONS & ENGINEERING ]LIVE

Forge™

Ship production software at AI-native speed. Agent swarms turn specifications into working systems — new applications, modernisations, integrations and services-as-software — with human review at the gates that matter, delivered in weeks and priced on fixed outcomes rather than billed hours.

FORGE // DELIVERY LINE

// Problem

The Problem

Enterprise software delivery is priced and paced by headcount, so the estimate is a function of how many people are available rather than how hard the problem is. Modernisation backlogs grow faster than they clear, integration work absorbs teams for quarters, and the projects that would actually differentiate the business queue behind the ones that merely keep it running. The commercial model compounds the problem: billing by hour rewards duration.

  • Delivery timelines scale with team size, not with problem difficulty.
  • Integration and modernisation work consumes the capacity meant for differentiation.
  • Time-and-materials contracts align the vendor's incentive against the client's.
  • Specification-to-code handoff loses intent at every translation step.

// Overview

Forge applies agent swarms to the delivery pipeline itself. A specification is decomposed and distributed across coding agents that generate, self-validate and integrate, with human engineers reviewing at defined gates — design, validation, deployment — rather than at every commit. The output is production software running in the client's environment, delivered on a fixed-outcome commercial basis. The same machinery covers greenfield applications, modernisation of existing systems, integration work, and services-as-software where a manual process is replaced by a running system.

// AI System

Why AI

Agent swarms change the economics of delivery because generation, validation and integration can proceed in parallel across a decomposed specification, while human attention concentrates at the points where judgment actually changes the outcome. Reviewing at gates rather than at every commit is the operative choice: it preserves engineering control over architecture and correctness without making a human the throughput limit on typing. That is what makes fixed-outcome pricing viable — the cost is no longer a linear function of duration.

// Specs

Specifications

SCOPE
New applications, modernisation, integration, services-as-software
METHOD
Agent swarms, spec-decomposed, self-validating
HUMAN CONTROL
Review gates at design, validation and deployment
DELIVERY
Weeks
COMMERCIALS
Fixed outcome, not time and materials

// Features

Features

  1. 01Specification decomposed and executed in parallel across coding agents.
  2. 02Self-validation inside the loop, so review receives working code rather than drafts.
  3. 03Human engineering review concentrated at design, validation and deployment gates.
  4. 04Covers modernisation and integration, not only greenfield builds.
  5. 05Services-as-software: manual processes replaced by running systems, not by documentation.
  6. 06Priced on delivered outcome, aligning vendor incentive with client timeline.

// Architecture

Architecture

DELIVERY FLOW

Runtime · one item, left to right


  1. 01Specification
  2. 02Decomposition
  3. 03Agent Swarm: Generate + ValidateApplicationModernisationIntegrationServices-as-Software
  4. 04Integration
  5. 05Human Review Gate
  6. 06Production Deploy

dashed = the inference step, where the system exercises judgment

System stack

Data in · decisions out

01

Sources

Specs, code, and the legacy estate

Requirements & specsSource repositoriesLegacy systemsCOBOL, Java, .NETDesign system & standardsTest suites

02

Ingestion

Understand before generating

Codebase indexingLegacy comprehensionLLM over source + docsSpec → task graphStandards encoding

03

Ontology

Delivery as traceable objects

RequirementTask · ChangeTestGateRelease

04AI

Intelligence

Agents build, gates verify

Coding agentsparallel, task-scopedTest generationStatic & security analysisModernisation translatorsEval suitegate pass rate

05Human

Human control

Gates are the control surface

Architecture gateSecurity gateRelease approvalEngineers own design

06

Actions

Written back

Pull requestsCI/CD pipelinesProduction releases

Observability

Every model call traced; evals run on real cases, not anecdotes.

Governance

Entitlements enforced at retrieval; rules versioned by the organisation.

Write-back

Systems of record are written only through the approval gate.

Gates are the control surface — architecture and correctness stay under human engineering authority.

// Impact

Impact

Weeks
Delivery, against quartersindicative target
Fixed
Commercial basis, not hourlyverified

Interested in Forge?

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// End of case studyForge™