Hexagon Systems · Artificial Intelligence

AI grounded in software engineering.

Artificial intelligence is becoming part of the software engineering toolkit. Its value depends on how it is incorporated into architecture, delivery practices, testing and operational controls.

THE EVOLVING AI ECOSYSTEM

A changing landscape of AI tools, models and interfaces. Names are illustrative, not partnership claims.

AI-assisted software delivery

AI can accelerate analysis, implementation, documentation and testing. The outputs still require sound requirements, meaningful review, reproducible evidence and clear human accountability.

Intelligent applications

Language models and related capabilities can be integrated with existing applications, APIs and enterprise data. Effective solutions depend on appropriate boundaries, evaluation, security and the handling of uncertain results.

Engineering the delivery process

AI changes the economics of producing software, but does not remove the need for architecture, integration, quality assurance, release governance or operational support.

Practical adoption

The right starting point is a specific engineering or business problem, with measurable outcomes and controls proportionate to the risk. We favour useful capabilities over demonstrations that cannot be maintained.

Engineering in practice

From an idea to a working system.

Three illustrative workflows demonstrate the engineering behind AI integration: an application request, a scripted verification and a structured response check.

AiClient.cs HTTP · JSON · cancellation

using System.Net.Http.Json;

using var client = new HttpClient();
using var request = new HttpRequestMessage(
    HttpMethod.Post, endpoint);
request.Headers.Authorization =
    new AuthenticationHeaderValue("Bearer", token);
request.Content = JsonContent.Create(new {
    model = selectedModel,
    input = "Summarise this incident"
});
using var response = await client.SendAsync(
    request, cancellationToken);
response.EnsureSuccessStatusCode();

Request handling · authentication · cancellation

verify.ps1 automation · quality gates

$ErrorActionPreference = 'Stop'

# Stop the pipeline if any stage fails
$steps = @(
    @{ Name = 'Restore'; Args = @('restore') },
    @{ Name = 'Build'; Args = @('build', '--no-restore') },
    @{ Name = 'Test'; Args = @('test', '--no-build') }
)

foreach ($step in $steps) {
    & dotnet @($step.Args)
    if ($LASTEXITCODE -ne 0) {
        throw "Failed: $($step.Name)"
    }
}

Repeatable builds · tests · delivery controls

evaluate.py validation · evidence

from dataclasses import dataclass

@dataclass
class Assessment:
    outcome: str
    evidence: list[str]
    confidence: float

def is_acceptable(result: Assessment) -> bool:
    return (
        result.outcome in {"pass", "review"}
        and bool(result.evidence)
        and 0.0 <= result.confidence <= 1.0
    )

# Model output is data to validate, not proof.
assert is_acceptable(Assessment(
    "review", ["tests", "trace-id"], 0.82
))

Structured output · validation · evidence

Technology references

Selected external documentation for readers who want to explore the technologies and practices discussed here. These links are informational and do not imply a commercial partnership.

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