Up to 7 phases. Scaled to the work.
The platform classifies intent complexity and right-sizes the pipeline — from a single-agent one-shot to the full 7-phase SDLC. Simple tasks get 1 phase. Moderate changes get 3-5. Complex multi-repo features get all 7 with parallel reviewers, planning, and verification gates.
From intent to pull request.
- 01Intake
One door in.
Every feature request, bug, migration, or refactor begins the same way — as a plain-language intent from Slack, Jira, the dashboard, or the API. The platform enriches it with AI, classifies scope, and produces a structured work item with confidence scoring and budget checks.
- 02Discovery
Understand before building.
AI-driven codebase research with per-repo freshness tracking. Impact analysis maps which repositories and files will be affected. Constraints, conventions, and prior decisions are pulled from the knowledge base and fed into downstream phases. This is the phase most AI tools skip entirely.
- 03Review
Five parallel reviewers before code is written.
Architecture review, security review, design review, verification strategy review, and delivery review run in parallel. Each produces a structured verdict: approve, approve with conditions, or reject. If any reviewer rejects, the pipeline halts. Code is not written until the plan has been reviewed.
- 04Planning
Structured plan with verification gates.
The platform constructs an execution plan from the reviewed work — what gets built, in what order, with structured checkpoints between each step. The plan is validated for conflicts and impact before execution begins.
- 05Execution
Specialized agents. Resilient execution.
Purpose-built agents execute each step of the plan. The runtime is model-agnostic — Claude, Llama, Mistral, or Nova, selected per stage. Automatic recovery from failures. State preserved at every boundary. Live progress streaming to the dashboard.
- 06Verification
Pass or halt. No exceptions.
Structured verification verdicts: pass, fail, flaky, blocked, insufficient evidence. Tests run. Contracts validate. Quality gates enforce. If a step fails, the pipeline stops, logs the failure, and escalates. Nothing proceeds until the previous step has passed.
- 07Delivery
Review-ready pull requests. Full audit trail.
Coordinated pull requests across every affected repository. Documentation generated. Changelogs written. The full Activity Stream is queryable — every agent decision, every state transition, every human override. The work arrives review-ready. Production-readiness is your review's decision.
What a running execution looks like.
Horizontal DAG on the left. Live agent feed on the right. Every agent decision streamed in real time with cost tracking.
Implementing POST /api/appointments/team-members endpoint
Creating TeamMemberAssignment component with form validation
Added AppointmentTeamMember model with foreign keys
Reading existing appointment detail page patterns
Execution plan approved — 3 parallel tracks
Approved — no new auth surfaces, existing RBAC sufficient
Approved with condition: add index on appointment_id
Approved — consistent with existing team management patterns
Mapped 4 affected files across 3 repos, 2 existing patterns
Distributed-systems engineering for agent execution.
Crash recovery, distributed locking, liveness detection, immutable audit trails. These are the mechanisms that make consistent output possible at scale.
Deterministic flow
Every work item follows the same path through the system. No shortcuts, no special cases. Consistent output from consistent process.
Structured checkpoints
Between each phase, structured contracts define what must be present before the next phase begins. Validated programmatically — no soft handoffs.
Built for reliability
Automatic recovery from failures. State preserved at every phase boundary. Stalled agents detected and restarted. Nothing is lost if something goes wrong.
Immutable audit trail
Every decision recorded. Queryable by execution, phase, agent, or outcome. Independently verifiable — your auditor can confirm the chain with no Spire access required.
Automation that works for your engineers.
Spire handles the implementation work that should not have required your engineers in the first place — migrations, framework upgrades, test backfills, API versioning, vulnerability patches. Your team stays in control at every boundary.
Plan review
Before code is written, the execution plan can be adjusted. Phases can be added, removed, or restarted. The graph is editable.
Approval gates
Configurable checkpoints where human approval is required. The pipeline waits. State is preserved. The reviewer sees full context.
Live intervention
Pause, redirect, or abort at any time. Escalations route to the right reviewer. Human judgment is a first-class input.
Post-delivery feedback
Multi-round feedback conversation to refine output. The feedback agent has full workspace access — reads, edits, runs tests, commits, and pushes. Costs tracked per round.
Beyond the pipeline.
The pipeline is the core. These are the surfaces that make it usable at enterprise scale.
Enterprise knowledge
Upload runbooks, design docs, policies, and wiki pages. Agents retrieve this context at execution time — reasoning over your documentation and your code. Designed for legacy codebases where institutional knowledge matters as much as the source.
Native Bedrock runtime
Runs natively on AWS Bedrock. No external dependencies. Model-agnostic — Claude, Llama, Mistral, or Nova at each pipeline stage. Fast, reliable, and everything stays in your VPC.
Enterprise SSO
Admin-configurable SAML 2.0 and OIDC from the settings dashboard — Entra, Google Workspace, Okta, or any provider. Under 5 minutes to configure. Standards-based security. Break-glass local admin always preserved.
Codebase Copilot
Project-integrated conversational AI. Searches your repos in real time, retrieves enterprise docs, and maintains context across long conversations. Propose work, review cost/time estimates, confirm, and watch execution — all in one continuous thread. Produces artifacts: PDFs, mockups, documentation, data exports. The conversation is the whole experience.
Integration ecosystem
Slack /spire commands, Jira sprint imports, MCP server connectors for databases, APIs, and external tools. Agents use your team's tools directly.
Vulnerability management
Import security findings, associate Jira tickets, and run structured fixes through the pipeline. Evaluate, dismiss, or fix — each action flows through the same path.
See it run on your code.
Bring a feature request from your backlog. We run it through the pipeline on your codebase. You see the pull requests at the end.