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Agentic Fleet for Support & Development

We've built AI agents that write, fix, review and document software across the delivery lifecycle, augmenting our engineers, never replacing them. A human stays accountable for everything that ships.

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Delivered in partnership with Techno Union
Watch it in action

See the agent fleet at work

How the agents triage, fix, review and document, with a human accountable at every gate.

The business problem

Delivery is slow and expensive

Building and maintaining software ties up senior engineers in repetitive work, fixing bugs, reviewing pull requests, and keeping documentation current. It's hard to move fast and hold quality at the same time.

  • Repetitive work consumes skilled engineers
  • Review and QA become bottlenecks
  • Documentation and knowledge fall behind
  • Speed and quality feel like a trade-off
What we have

A team of development agents

Language-model-driven agents handle meaningful steps of the lifecycle inside a structured, deterministic process, with humans in control of what ships.

  • Every agent has clear inputs and outputs
  • Bounded tools and permissions
  • Fully observable, every action logged
  • Agents that act never review themselves
Agents in operation

Four agents, working today

Built for a Course Feasibility programme at an educational institution and now deployable inside client organisations.

Bug-Fix Agent

● Live

Pulls the issue, recreates the problem on real systems, ships a fix and tests, gets a second-model cross-check, then opens a PR or escalates with the full reasoning trail.

Fixing & testing

PR Review Agent

● Live

Read-only. Reads the diff in context and checks correctness, quality, architecture and security, then posts findings straight back to GitHub.

Reviewing diffs

Knowledge Refresh Agent

● Live

Scheduled. Keeps the codebase index, architecture docs and lessons-learned index current, so later agents start from accumulated experience.

Indexing knowledge

Codex Rescue

● On-call

Invoked when the primary agent is stuck or a second implementation pass helps, a different model has different blind spots.

On standby
Inside the agent fleet

How the agents work together

Two views: the high-level workflow across the whole support and development lifecycle, and a closer look at how the Bug-Fix agent decides. A human stays accountable at every gate.

SIGNAL SOURCESSupport ticketsAlert emailsLogic Apps & ADFLogs & App InsightsService Bus DLQWorkload failures1Health Checkscans · exceptions2Triageclassify · severityKnown issueor new?3Bug-Fixerreproduce · PR4Reviewerquality · securityHuman gatea person approvesCI / CDbuild · scan · deploy5Knowledgewrites lessonexceptionnew bug / changeapprovedSYSTEMS OF RECORDJira · historyAzure DevOpsMicrosoft TeamsKnowledge baselesson feeds the next triageseen before → attach known fix, close
End-to-end agentic workflow, signal sources in, systems of record connected, every change gated by a human.
Agent-supported operations

Five agents across the support lifecycle

Beyond development, the same patterns run day-to-day operations, each agent has one job, a fixed action boundary, and produces a proposal. Nothing acts on its own.

AgentWhat it doesAction boundary
Health CheckRuns scheduled checks across the estate; publishes an exception-only report.Read-only telemetry
TriageReads the ticket and telemetry; writes enrichment fields only, behind approval.Read-only ticket & telemetry
Bug-FixerReproduces and drafts a fix for an approved case.Draft PR / patch only
ReviewerReads the proposed change; posts findings on correctness, quality, architecture and security.Read-only review
Knowledge-RefresherDrafts lesson and runbook updates for approval.Read / write · drafts
Governed by design. The Agent Operations Board sits across all five, one view of activity, outcomes, approvals, escalations and metrics. Every output waits for human approval, secured with Entra ID, managed identities, RBAC, Key Vault and API Management guardrails.
Signal intake · inbox · tickets · alerts · health checks›Signal router›Agent runtime · Microsoft Foundry · Agent Framework›Grounded · Azure OpenAI · AI Search over runbooks & code›Governed outputs · ticket enrichment · draft PRs · knowledge drafts · notifications›Human approval›Operations Board
The lifecycle

Every stage is gated, nothing ships without passing through

Each stage produces a typed artefact. On failure it auto-corrects, tries again, then escalates to a human with the full trail.

STAGE 1

Spec

Converts a ticket into a typed, testable specification.

STAGE 2

Build

Executes the task graph across parallel, isolated worktrees.

Live
STAGE 3

Review

Four reviewers run in parallel, correctness, quality, architecture and self-review.

Live
STAGE 4

Deploy

Tests, security scan, canary smoke and human sign-off at every boundary.

STAGE 5

Knowledge

Every run leaves the system smarter, lessons indexed and consulted next time.

Live
What makes it reliable

Structured, observable, accountable

Humans stay accountable

Agents augment the team; a person is accountable for everything that ships.

Review and build stay separate

Agents that act don't review themselves, a clean separation of duties.

Confidence-gated escalation

Low-confidence work is flagged and reassigned with the reasoning trail attached.

Knowledge compounds

Every change writes a structured lesson that future runs consult before they start.

Fully observable & audited

Every tool call, model call and decision is logged, full lineage in Azure SQL.

Microsoft-native substrate

End-to-end on the Azure stack, no re-procurement or second vendor.

Microsoft Agent FrameworkMicrosoft Agent FrameworkAzure OpenAIAzure OpenAIAzure FunctionsAzure FunctionsDurable FunctionsDurable FunctionsAzure AI SearchAzure AI SearchAzure SQLAzure SQLMicrosoft Entra IDMicrosoft Entra IDBicep + GitHub ActionsBicep + GitHub Actions
The impact

Proven in a live testing window

40%Reduction in delivery cost
90Bugs completed by the agent in one SIT window
96%QA acceptance rate
100%Model interactions captured & audited
See it in action

The agents at work

A live view of our Bug-Fix, PR Review and Knowledge Refresh agents, completed fixes, resolution breakdown, daily merge volume and open escalations.

Bug Fix AgentPR Review AgentKnowledge Refresh
Completed
90
99% of 91 tracked
In progress
1
1 active · 0 esc · 0 v-fail
Unique PRs
70
grouped fixes counted once
Deployed to dev
59
of 90 completed
Effort saved
296h
≈ A$41,440 at A$140/hr
Verified
0
not yet run
QA accepted
79
96% of 82 decided
Pending QA
0
of 84 eligible
QA rejected
3
4% rejection rate

Resolution breakdown

Merged via own PR
71
Reconciled
8
Shared/grouped PR
3
Duplicate
2
On-hold (manual)
2
Escalated
2
Won't fix
1
Requirements gap
1

Daily merge volume

Last 11 days
04-15
04-16
04-17
04-18
04-20
04-21
04-22
04-23
04-24
04-25
04-26

Active fixes

BugStatusAreaConfidencePR
SAFMP2-611fix_failed———

Open escalations

No open escalations.
Illustrative snapshot of the Bug-Fix Agent control board.
Bring it into your org

Deploy agentic development in your teams

We can stand up the same agents inside your delivery lifecycle, safely, observably, and with your engineers in control.

Get in touch →