Multi-Agent SDLC Assistant

From a business document to reviewed, production-ready code.

An eight-stage agent pipeline with four human approval gates. Built at IWConnect, deployed inside your environment.

Enterprise Data AI — Semantic Layer

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    The drift

    Business documents arrive. Six weeks later, what ships isn't quite what was asked for.

    Specs drift. Stories get loose interpretation. Architecture decisions land inside pull requests instead of approval gates. By the time the build is reviewable, the original requirement and the delivered code have separated, and reconciling them costs another sprint.

    Adding more reviewers slows the team. Adding more documentation goes stale on day two. The cost of doing nothing is a ship date that quietly slips two quarters.

    What this is

    Eight agents. Each with one job. Four human gates between them.

    The Multi-Agent SDLC Assistant takes a Business Requirements Document and produces a structured spec, INVEST user stories, Jira tickets, an approved technical plan, an interactive mock design, and a runnable React + Node monorepo. Every output sits behind a human approval gate before the next agent runs.

    It's built for engineering organisations running structured SDLC on Jira, where requirements arrive as business documents and quality gates already matter.

    It's not for teams in regulated industries that cannot send code or requirements to external model providers. That constraint will come up in the pilot scoping call before any deployment.

    What changes: the audit trail from a line in the BRD to the merged code is one continuous record, attached to the Jira tickets it produced.

    How it works

    Eight stages. Four human gates. One auditable run.

    Each agent does one job and hands its output to the next. Reviewers approve, edit, or reject at every gate before the next stage runs.

    01

    Requirements Analyst

    A messy BRD becomes a structured spec the team can review on one page. When the BRD is ambiguous, the analyst asks back rather than fabricating an answer.

    frontier model
    02

    Story Generator

    The spec breaks into 4-6 user stories that pass INVEST checks.

    frontier model
    03

    Jira Sync

    Each approved story lands as a Jira ticket in the right project, with the spec ID attached.

    tool · jira-rest-v3
    04

    Story Judge

    Stories are scored against template rubrics using RAG. Weak stories surface before planning starts.

    frontier model
    05

    Tech Planner

    Approved stories become a Speckit-style markdown plan you can read, edit, and approve.

    frontier model
    06

    Mock Design

    An interactive SPA mockup of the screens, built from the BRD plus the plan. Show it to the business sponsor and confirm scope before the Implementer runs.

    frontier model
    07

    Implementer

    A runnable React + Node monorepo, generated against the approved plan.

    frontier model
    08

    Report

    Every phase, decision, token count, and cost from the run, attached to the Jira tickets it produced.

    tool · langfuse-aggregator

    The four human gates

    01
    Spec approval After Requirements Analyst
    02
    Plan approval After Tech Planner
    03
    Mock design approval After Mock Design
    04
    Code review After Implementer
    What reviewers can actually do

    Approve from the desk. Approve from the phone. Same trace either way.

    Six capabilities every reviewer gets in their first week.

    See the run live

    A Mermaid trace shows every node as it runs. Amber for active, green for done, dashed red when the pipeline is waiting on a human.

    Approve from anywhere

    Mock Design and Implementer take minutes to run. Reviewers don't sit waiting. Push notifications on iOS and Android deep-link straight into the gate that needs you.

    Edit the agent prompts

    Every prompt is versioned in Langfuse and editable from the UI. Changes hot-reload on the next run, no redeploy.

    Read the full diff

    Syntax-highlighted diffs on web or mobile. Approve the code, request changes, or block the merge.

    Inspect the audit trail

    Web and mobile approvals show up in the same timeline. One run, one record, one auditor view.

    Stop the build

    Every gate is blocking. Nothing reaches the next agent until a human approves. No silent overrides. Runs are checkpointed, so a paused pipeline resumes from the last approved gate.

    Quality and safety

    Five judges. Four dimensions. Zero leakage in the injection harness.

    Every agent output is scored against a rubric before it can move forward. Adversarial prompts are replayed through the pipeline on every release.

    5 Agents scored Spec, stories, plan, code, UX. Every output graded on every run.
    4 Evaluation dimensions Completeness, correctness, consistency, safety.
    10/10 Injection payloads Adversarial prompts including key extraction, environment-variable readout, and script injection attempts. Zero leakage to date.
    7/10 Pass threshold Below this, the build fails automatically. No overrides.
    Security and governance

    An audit trail you can hand to a board.

    Every run is recorded in Langfuse: which agent ran, which prompt version it used, what it produced, which tools it called, and which human approved the next step. The trail covers web and mobile approvals as one timeline.


    Prompts are versioned, editable from the UI, and hot-reloaded on the next run. The build fails automatically if any agent output scores below the pass threshold.


    Deployment is inside your environment as part of the pilot. Six of the eight agents call frontier models from multiple providers. The seventh and eighth are deterministic tools (Jira REST for ticketing, Langfuse for tracing). Which provider runs which agent is documented in the audit trail and is swappable to match your data-handling constraints.

    Early access pilot

    What a pilot actually looks like.

    An IWConnect engineering team installs the pipeline inside your environment, connects it to your Jira, and runs it against one of your existing BRDs.

    The pilot covers deployment, connector configuration, prompt tuning to your spec template, reviewer accounts for web and mobile, and a debrief at the end. By the end of it, your team has a working pipeline against a real requirement, the audit trail of that run, and a clear answer on whether the rest of your roadmap belongs in this pipeline.

    What we ask from you: a real BRD, one or two reviewer-level engineers for the gates, and a 30-minute scoping call to confirm fit.

    Book a pilot scoping call
    Early access

    Bring us one BRD. We'll bring you the pipeline.

    A 30-minute scoping call covers fit, data-handling constraints, and the BRD you'd run through the pipeline first. We deploy inside your environment, run it once with your reviewers, and you keep the audit trail either way.

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