Agentic software engineering across the entire SDLC
SPECTRA implements fully agentic workflows across the entire SDLC — turning product intent into working software through a disciplined, human-gated loop where AI agents do the heavy lifting and engineering team approves each phase as it goes. Standards encoded once, impact measured end-to-end, delivered with hands-on enablement.
Vibe coding and plan mode — Claude Code, Cursor, and the rest — supercharge a single phase. But each phase runs in its own session: the agent rebuilds its understanding from scratch every time, and the spec, design, tests, and code drift apart at every hand-off. That's AI-assisted — a faster typist. True AI-native engineering keeps one context intact across the whole lifecycle, so every phase builds on the last instead of starting cold.
SPECTRA makes spec-driven development truly AI-native. Every phase reads and writes one shared, durable context — the spec, plan, design, tasks, and code stay in lockstep through a tight, repeating loop, with a human owning the gate at each step. Because that context is shared, every agent stays in full compatibility with the standards and guardrails set for the system — nothing drifts out of policy from one phase to the next. Continuity is the design goal; speed and quality follow from it.
Spec, plan, design, tests, and code all live in and update the same context, so they can't silently diverge — the spec stays the contract and everything traces back to it.
Each phase inherits everything the last one knew — intent, constraints, prior decisions — so the agent reads the answer instead of guessing it.
Decisions and the trade-offs behind them are captured in the durable context, so settled questions stay settled and a deliberate choice is never mistaken for an accident.
Continuity plus a human gate at each step surfaces a misread where it's cheap to fix, before it propagates into implementation and test.
The agentic engine does the drafting and the building. What makes SPECTRA enterprise-grade isn't only the method, the standards, and the gates around it — it's multi-agent orchestration and agentic AI engineering, with each agent tailored and designed for a specific phase or task, covering the SDLC end-to-end.
SPECTRA is built on GitHub Spec Kit — the open-source toolkit that brings spec-driven development to AI coding agents. That methodology is a proven discipline for turning intent into working software, and it complements AI exceptionally well.
On that foundation, SPECTRA adds its multi-agent orchestration layer, domain constitution library, and SPECTRA Delivery Intelligence — the layers that make it enterprise-grade.
SPECTRA is two things working together. The SPECTRA Agents run the fully agentic SDLC. Alongside it, SPECTRA Delivery Intelligence puts the four DORA metrics in front of every team — and it does so not by reinventing the wheel, but by standing up a proven, best-in-class observability platform (Datadog, Dynatrace, New Relic, or an open-source Grafana + Apache DevLake stack), wired to the tools teams already use. So every team sees delivery performance end-to-end, knows where to improve, and can tell whether changes are actually working.
Multi-agent orchestration that runs the agentic SDLC — the loop, the specialist agents, and the human gates — across every phase.
We don’t reinvent observability — we stand up a proven platform (Datadog, Dynatrace, New Relic, or Grafana + DevLake), wired to the tools you already use, so delivery performance is visible and provable.
Instead of one generalist doing everything, SPECTRA comes with a roster of agents — each optimized for a single job and primed with the project's (or organization's) standards. Every SPECTRA agent runs on the coding agent the team already uses.
Every SPECTRA roster is built from two kinds of agents — a required core that runs the SDLC end-to-end, plus optional add-ons you switch on as the domain demands.
The backbone of every SPECTRA implementation. Core agents run the spec-driven SDLC end-to-end — requirements, architecture, implementation, testing, and deployment. Every engagement ships with the full core roster.
Optional specialists you switch on as the work demands — they audit, validate, and add more to the context. Compliance, privacy, security, and quality checks layered onto the core flow; pick only the ones your project needs.
Encodes your coding, security, and architecture standards once — so every agent downstream inherits them automatically.
Reads your codebase and existing docs, then suggests the guardrails that best fit your business domain. SMEs review and accept what they need — the approved set flows into the constitution.
Checks electronic-records and e-signature integrity (Part 11) and medical-device software-lifecycle rigor (IEC 62304) — traceability, audit trails, and risk files mapped to software safety class.
Audits ISMS and privacy-management controls against Annex A, reusing shared evidence so one control can satisfy SOC 2, ISO, and HIPAA at once.
Turns a BRD or product brief into structured user stories with clear, testable acceptance criteria.
Turns a raw business requirement — typed text or a .docx, .pdf, .md, or .txt document — into a structured, specify-ready BRD. Reads project context and asks clarifying questions only where the requirement has gaps, never inventing requirements.
Interrogates vague or missing requirements up front, before they turn into expensive rework later.
Scores the spec for completeness, clarity, and consistency — effectively unit tests for your requirements.
Verifies data-subject rights, lawful basis and consent, data minimization, retention and erasure, and cross-border transfer — and scaffolds the Article 30 records of processing.
Evaluates Canada's federal and provincial privacy duties — PIPEDA's fair-information principles plus Quebec Law 25's mandatory PIAs, privacy-by-default, and cross-border assessments.
Classifies AI components by risk tier and assembles the transparency disclosures and Annex IV technical documentation the EU AI Act requires.
Turns regulatory and contractual text into testable acceptance criteria — the connective tissue that lets any new regime flow into the spec and the compliance agents.
Produces the technical plan and tech-stack decisions, choosing the design patterns that fit the problem — not the hype.
Captures context-aware architecture decisions grounded in your codebase, prior ADRs, and the project constitution — and flags any constitution update the decision implies.
Audits the design against best practices, design principles, and your own standards before a line is written.
Audits PHI handling against the Security Rule technical safeguards — access control, audit logging, integrity, authentication, and transmission encryption — and maps gaps to §164.312.
Scopes the cardholder-data environment and checks secure-development, storage, transmission-crypto, and testing controls against PCI-DSS v4.0.1.
Generates design-time STRIDE and attack-surface analysis from data-flow and architecture, complementing the runtime focus of the Security Analyst.
Static hot-path, complexity, and N+1 analysis with load-model sanity checks, surfacing scalability risk before the build.
Discovers PII and PHI across code and schemas, maps data flows and lineage, and classifies data — feeding the privacy and HIPAA agents.
Lints OpenAPI specs, detects breaking changes, and enforces versioning and backward compatibility.
Breaks the plan into an ordered, dependency-aware task list — and can sync it straight to your issue tracker.
Cross-checks spec, plan, and tasks for drift, gaps, and contradictions before the build kicks off.
Executes the task list in dependency order, building to spec with tests written alongside the code.
Generates an SBOM, runs reachability-aware vulnerability and license analysis, and flags transitive supply-chain risk.
Reviews schema design, migration safety, and indexing — flagging lock risk and backward-incompatible changes before they ship.
Assesses API doc coverage, README and runbook completeness, and drift where the code changed but the docs didn't.
Quantifies complexity, duplication, dead code, and code smells into a maintainability rating and remediation estimate.
Generates unit, integration, smoke, and end-to-end tests, each mapped back to an acceptance criterion.
Finds the gaps against the test pyramid, so coverage is real protection — not just a percentage.
Recommends what's worth automating and where each test should run across the pipeline.
Surfaces threat exposure and OWASP-class issues through static and dynamic analysis of the change.
Audits the UI against WCAG 2.2 AA and maps conformance to the laws that adopt it — ADA, Section 508, and EN 301 549 — then scaffolds a VPAT.
Estimates software carbon intensity using the ISO-standard SCI methodology and surfaces the efficiency hotspots that move it.
Flags hardcoded strings, locale and RTL handling, and un-externalized resources so the product is ready to localize.
Audits ML components for bias, fairness, and explainability, and scaffolds the model card.
Opens a correctly-targeted pull request for the completed spec — deriving the base branch from your promotion strategy, confirming before any push, and returning the PR link.
Continuously analyzes logs, latency, and error signals post-deployment; surfaces anomalies and predicts SLA violations before they impact users.
Correlates incident signals with recent deployments, recommends a targeted rollback or fix, and validates the resolution against the original spec.
Maps controls to the AICPA Trust Services Criteria and assembles continuous, change-managed evidence — every control change traceable to a commit.
Validates segregation of duties and change-approval evidence for financially relevant systems, producing an immutable release-approval trail.
Detects Terraform, CloudFormation, and Kubernetes misconfigurations and drift, mapped to CIS, PCI, and SOC 2 baselines.
Estimates cloud cost from IaC, flags right-sizing and waste, and shows the cost delta of each change.
Checks whether logs, metrics, and traces are instrumented, SLOs defined, and alert coverage adequate against the golden signals.
Measuring DORA is a solved problem — so we don’t rebuild it. SPECTRA Delivery Intelligence stands up a proven, best-in-class observability platform, wires it to the tools your teams already use, and tunes it to your own DORA baseline — enterprise-grade delivery metrics on day one, not a bespoke dashboard we’re still building.
The DORA engine, the connectors, the scoring — that’s the job of platforms already proven at scale. Our value is picking the right one for each client, standing it up, integrating it with their toolchain, and running it — on a stack you own or as a service we manage for you.
Representative view — rendered on the observability platform we stand up for you
We deploy a battle-tested observability platform — Datadog, Dynatrace, New Relic, or open-source Grafana + Apache DevLake — not a dashboard built from scratch, then wire in the tools you already use (GitHub, Jira, PagerDuty and more).
Deployment frequency, lead time, change failure rate, and time to recovery — computed live, with speed paired to stability.
Every metric is scored Elite → Low on the industry-standard scale, so where a team stands is never ambiguous.
Deltas against your own baseline show whether changes are working — the feedback loop that drives continuous improvement.
AI-DLC is AWS's AI-Driven Development Lifecycle — introduced to fix a structural limit of the traditional SDLC: it's built around humans, with AI bolted on at the edges. AI-DLC inverts that and puts AI at the centre — AI drafts the plan and does the heavy lifting, while humans review and approve at each gate. That is exactly the SPECTRA model. SPECTRA already runs this way on a conventional SDLC, and it's ready for AI-DLC out of the box: its loop, shared context, and human gates map straight onto AI-DLC's three phases — so adopting SPECTRA is itself how a team makes the shift, without giving up the discipline that keeps quality high.
Inside every phase the same pattern repeats — the team validates at a gate, AI drafts and builds, the team reviews — run as short bolts (hours or days, not week-long sprints). The same agents defined earlier slot straight into the AI layer below — no new roster, no rework. Here's who does what, phase by phase:
Adoption is a roadmap, not a switch. First we agree the plan together — which team runs the POC, the timeline through early-adopter waves to org-wide rollout, and how SPECTRA Delivery Intelligence is hosted. Then we prove the loop on one contained project, measured against that team's own DORA baseline, before expanding in waves — coaching teams into the loop, not just handing out licenses.
Together we pick the POC team and project, set the timeline through to org-wide rollout, and plan how Delivery Intelligence is deployed — in your environment or run by us as a managed service.
Joint planningWe embed SPECTRA in one team's existing lifecycle on a small, contained feature. We baseline their DORA metrics first — capturing them with you if needed — run the loop alongside the team, and close with a before/after comparison that proves the gain.
6–10 weeksWith the POC proven, we roll the loop out to 3–6 teams or projects — training and coaching each one, and staying engaged in an advisory role as they ramp.
3–6 teamsWe turn the early adopters' learnings and challenges into an organization-wide rollout plan — until the loop is the default way the org builds.
Org-wideSPECTRA brings a spec-driven workflow your teams can trust — with the loop, the standards, and the human gates that make it safe to scale.
Get started →