Back to homeThe SDLC built forAI agent pipelines.
HivePipe replaces the gap between product intent and shipped code with a structured, agent-orchestrated delivery pipeline. PRDs, task plans, validation, and pull requests stay connected from brief to merge.
Structured intent, not prompt chaining
Start from a product brief and get a PRD with acceptance criteria, affected files, test expectations, and technical constraints — before any agent writes a line of code.
Specialized agent lanes
Discovery, implementation, validation, and documentation run in distinct phases with reviewable handoffs, not one opaque AI loop.
Governance at every gate
Approval checkpoints, role-aware controls, and audit trails give engineering leaders confidence that agent output meets production standards.
Git-native from day one
Work lands as reviewable pull requests with context, not as AI-generated blobs that bypass your existing review culture.
The problem
AI code generation without structure is just faster chaos.
Most AI tools generate code from prompts but skip the engineering discipline that makes code shippable — requirements, validation, review, and traceability. HivePipe structures the entire path from product intent to merged PR with specialized agent phases, approval gates, and audit trails so teams move fast without cutting corners.
How it works
From brief to merge in structured agent phases.
01
Capture intent
Describe the feature in plain language. HivePipe builds a structured PRD with acceptance criteria, affected files, and technical constraints before any agent touches code.
02
Discover and design
Specialized discovery agents analyze your codebase, map dependencies, and produce a task plan aligned to your architecture and test expectations.
03
Implement with validation
Implementation agents execute tasks in phased lanes. Each phase has reviewable output, test coverage expectations, and approval gates before the next phase starts.
04
Merge with confidence
Final output lands as a pull request with full context — linked PRD, test results, and audit trail — ready for human review and merge.
Capabilities
Everything an agentic SDLC needs to ship production code.
Phased agent lanes
Discover, design, implement, validate, finalize, and integrate run as distinct phases with reviewable handoffs — not one opaque AI loop.
Structured PRD generation
Every delivery starts with a PRD containing acceptance criteria, affected files, test requirements, and technical constraints — generated from a plain-language brief.
Codebase-aware planning
Agents analyze your architecture, existing patterns, and file structure before proposing changes, reducing rework from context-blind suggestions.
Approval gates at every phase
Human review checkpoints between agent phases ensure nothing ships without engineering sign-off. Pause, redirect, or reject at any stage.
Validation-first execution
Test expectations are captured in the PRD and enforced during implementation. Agents validate against the spec, not just syntax.
Audit trail and traceability
Every agent action, phase transition, and approval decision is logged — giving compliance and security teams the evidence they need.
Multi-provider flexibility
Bring your own API keys for Claude, GPT, Gemini, OpenRouter, or Azure Foundry — with Amazon Bedrock coming soon. Choose the best model for the job.
Git-native output
Work lands as reviewable pull requests with full context, not as AI-generated blobs that bypass your existing review culture.
Founder credibility
Built by someone who has shipped 50+ products the slow way.
After 20 years as a CTO across SaaS, logistics, enterprise, and regulated teams, the same handoff failure kept showing up: great intent went in, diluted delivery came out. HivePipe is the workflow built to collapse that gap.
50+ platforms delivered
20+ years leading product teams
15+ organizations served
200+ remote staff managed
“Consistently translated ambiguous business intent into disciplined technical delivery without losing speed.”
Operator feedback from prior CTO engagements
“The rare mix of product judgment, engineering structure, and executive-level communication.”
Leadership recommendation theme across long-term partnerships
Frequently asked questions
What makes an agentic SDLC different from AI code generation?+
AI code generators produce code from prompts. An agentic SDLC orchestrates the entire delivery lifecycle — from requirements capture through implementation, validation, and merge — with specialized agents handling each phase. The key difference is structure: phased execution, approval gates, and traceability from intent to merged PR.
How do approval gates work between agent phases?+
Each phase transition (e.g., discovery → design → implementation) can require human approval before the next phase begins. Engineering leads review the agent's output, approve or reject, and the pipeline continues or pauses accordingly. This prevents runaway automation and keeps humans in control of production-bound work.
Can I use my own LLM provider with HivePipe?+
Yes. HivePipe supports bring-your-own-key (BYOK) for Claude, GPT, Gemini, OpenRouter, and Azure Foundry, with Amazon Bedrock coming soon. Keys are encrypted at rest with AES-256-GCM. Your team chooses which provider and model to use for each project.
How does HivePipe handle validation and testing?+
Test expectations are captured in the PRD alongside acceptance criteria and technical constraints — before any agent writes code. During implementation, agents validate their work against these expectations. The final pull request includes test results and coverage data as part of the review context.
Is HivePipe suitable for enterprise teams with compliance requirements?+
HivePipe is designed with enterprise governance in mind: role-based access control, approval gates, full audit trails, org-scoped data isolation, and encrypted API key storage. SOC 2 readiness is built into the product roadmap. The platform gives security and compliance teams the evidence they need to approve AI-assisted delivery.
See how HivePipe compares
Reserve your spot before the first rollout wave fills.
Founding members get early onboarding, direct product feedback loops, and a faster path to production use once the private beta opens wider.
87 spots remaining
Private beta for founders, CTOs, and engineering leaders.