Intelligence that compounds.

What Astrohive Does

Unlike tools that automate a single task, Astrohive agents coordinate across the full pipeline: research, strategy, design, build, test, and measurement. They learn your architecture, enforce your conventions, and improve over time. The intelligence compounds because every stage feeds the next.

Every agent operates under a configurable trust spectrum. You control how much autonomy each agent has, from passive observation to fully autonomous orchestration. Trust is earned through demonstrated accuracy, not granted by default.

How It Works: The Experiment Engine

Most AI tools do the same thing every time. Astrohive treats every action as a potential experiment with a measurable outcome. This is the core of what makes the platform compound.

The system identifies mutually exclusive experiments it can run in parallel without one spoiling the outcome of another. Each experiment has a predicted outcome backed by an evidence trail. When the experiment runs, the real-world result either strengthens or weakens the prediction. That difference is signal. Signal compounds. After enough cycles, the system develops a genuine model of what works for your specific business, not generic best practices.

See the full explanation of the experiment engine, including real examples across product, engineering, and strategy.

Trust Spectrum

Agent autonomy is controlled through four levels. Trust is per-domain and decays after 90 days of inactivity.

Problems We Solve

“AI code ignores our architecture”

Convention enforcement agents that learn your patterns and reject violations before code is committed.

“We cannot tell if AI output is good”

Eval framework with pass^k metrics, pre-committed decision criteria, and quality gates at every stage.

“AI costs are out of control”

6-layer optimization stack achieving 92% cost reduction through model routing, prompt caching, batch processing, and smart retrieval.

“AI agents are not secure enough”

Infrastructure-level security inheritance. Agents inherit your existing permissions at four enforcement checkpoints. Prompt instructions are not the security boundary.

“Context is lost between sessions”

Three-tier memory architecture: focused working context, retrieval layer for relevant history, and auditable archive for compliance.

“We need multiple agents coordinated”

Orchestrator pattern with trust spectrum controls. Agents are sovereign microservices that coordinate through defined protocols, not a single monolith.

“AI projects stall between pilot and production”

FDE model embeds a technical person with your team. Incremental deployment: agents start as observers, earn autonomy. Production-grade infrastructure from day one.

“We cannot monitor what agents do”

Three-layer observability stack: decision traces, performance metrics, and business outcome correlation. Debug opaque reasoning with full audit trails.

Research

Deep dives into enterprise AI adoption challenges. Each article answers a specific question CTOs face when deploying AI agents.

Team

Read our long-term vision

Frequently Asked Questions

What does Astrohive do?

Astrohive is an AI orchestration platform that turns AI into something actually useful for businesses. We deploy specialized agents across your entire product development lifecycle, not just code generation, but research, prioritization, design, build, test, measurement, and the feedback loop that connects them all. The agents connect to your existing tools, learn your specific context, and compound intelligence over time. What makes this compound is our experiment engine: the system identifies mutually exclusive experiments it can run in parallel, each with a predicted outcome backed by an evidence trail. Every experiment creates signal that strengthens or weakens what the system has learned. Over time, those signals compound into recursive self-improvement, where the platform doesn't just do what you tell it but gets measurably better at knowing what to do next.

How is Astrohive different from Cursor, Copilot, or Devin?

Those tools automate one slice: writing code. Astrohive orchestrates the full lifecycle: what to build, how to build it, whether it worked, and what to do next. But the real difference isn't coverage, it's learning. Every other tool does the same thing every time you use it. Astrohive identifies experiments it can run across your workflow, each with a predicted outcome and an evidence trail. Those experiments create signal. Signal strengthens or weakens what the system knows. Over time, the system doesn't just execute, it develops genuine understanding of what works for your specific team, codebase, and users. No competitor has this because it requires the full lifecycle (you can't measure outcomes if you only write code) and the trust spectrum (agents earn the right to run experiments through demonstrated accuracy). We're also model-agnostic. We don't care which model. We care about results.

What is the three-layer architecture (L1/L2/L3)?

Layer 1: agent baseline instructions (our IP, generic, reusable across all clients). Layer 2: client knowledge injected at runtime (your design system, conventions, architecture, business context). Layer 3: orchestration configuration (pipeline order, approval gates, routing rules). This separation means improving an agent benefits all clients, onboarding a new client is just a new L2 bundle, and changing how work flows is an L3 config change, not a rewrite.

How does the trust spectrum work?

Every agent starts as an Observer: it watches, learns your context, and can't take action. As it demonstrates accuracy in its domain, it earns promotion. Advisor: it suggests improvements but can't execute. Executor: it acts within defined boundaries, with humans reviewing output. Orchestrator: it coordinates multi-step workflows and manages other agents. Trust is per-domain (an agent trusted for code review might still be an observer for architecture decisions) and decays after 90 days without revalidation. This isn't just a safety mechanism. It's how we build confidence systematically. The operator invests in making agents good, which creates stickiness, quality, and the proprietary performance data that makes everything better.

How do you connect to our existing tools?

Via MCP (Model Context Protocol) and A2A (Agent-to-Agent protocol). We integrate with Jira, GitHub, Figma, Slack, and any MCP-compatible tool. The agents inherit your existing permissions at infrastructure level, not through prompt instructions. No migration required. The system wraps around your current stack and adds the intelligence layer that's missing.

Is my data safe with Astrohive?

Client data is physically isolated in separate embedding namespaces. No cross-tenant query paths exist architecturally, not just by policy. Agents operate in kernel-level sandboxes where the operating system prevents unauthorized access, not prompt instructions. API data is retained for 7 days only and never used for model training. We're building toward SOC 2 Type II certification with architecture designed for compliance from day one.

Can your agents access data they shouldn't?

No. Security is enforced at infrastructure level across four checkpoints: the prompt gateway (is this user allowed to ask this), the retrieval layer (can this agent see this data), the tool execution layer (can this agent call this API), and the output layer (does the response contain data above the user's clearance). If any checkpoint fails, the action is blocked. We don't rely on asking agents to be careful. We make it physically impossible for them to access what they shouldn't.

How do you handle experiments and learning?

This is the core of what makes the platform compound. The system identifies mutually exclusive experiments it can run in parallel without one spoiling the outcome of another. Each experiment has a predicted outcome with an evidence trail showing why we expect that result. When the experiment runs, the real-world outcome either strengthens or weakens the prediction. That's signal. Signal compounds. Concrete example: your team needs a new onboarding flow. Instead of designing one and hoping it works, the system designs two variants based on evidence from your existing user data, instruments both for measurement, and runs them as a parallel experiment. After 2 weeks it knows which variant drives higher completion rates and faster time-to-value for your specific audience. The losing variant gets deprioritized. The winning patterns get reinforced with evidence. This is recursive self-improvement in practice: experiments create signal, signal refines predictions, better predictions design better experiments.

Can I bring my own models or tools?

Yes. The architecture is model-agnostic by design. Each agent is a sovereign microservice that selects its own model based on task requirements. We have optimized configurations across Anthropic Claude, OpenAI, and open-source models. We don't care which model we use. We care about results. In the future, clients will be able to deploy agents using their preferred models and even bring their own agents that implement our MCP interfaces.

What's your business model?

Two predictable components. First, platform access: a subscription for the AI orchestration layer, agents, and the intelligence that compounds across your workflows. Second, FDE support: a Forward Deployed Engineer who embeds with your team to configure, optimize, and ensure you're getting maximum value. Both are predictable monthly fees. The FDE component scales down as your team becomes self-sufficient and agents earn higher trust levels. We use the best available models and tools regardless of provider, and we optimize aggressively so your AI costs stay manageable (our 6-layer stack achieves 92% cost reduction).

How much does Astrohive cost?

Our costs are comparable to hiring a 2-5 person consulting team to support digital transformation. The difference is what you get back. Astrohive doesn't just add headcount, it augments your entire team across multiple functions simultaneously. The platform pays for itself through measurable savings and output improvements across research (automated market and competitor analysis), design (faster iterations, design system enforcement), specs and requirements (first-pass specs that reduce developer rework), development (agents that help developers write code faster), QA and testing (fewer bugs reaching production, mutation testing), marketing (copy generation, spend optimization), and analytics (automated dashboards, experiment measurement). We also optimize aggressively on the AI infrastructure side (our 6-layer stack achieves 92% cost reduction on inference), so platform costs stay manageable as usage scales.

What's the ROI?

The ROI comes from two places: cost displacement and output multiplication. Cost displacement: if you're paying for researchers, BAs, QA teams, marketing copywriters, and data analysts across your product org, the platform handles significant portions of that work. Not replacing those people, but reducing the mechanical work so they focus on judgment and strategy. Teams we target are typically spending 7-15x our engagement cost across those functions already. Output multiplication: the same team produces 1.5-3x more output. Developers get pre-analyzed specs with less ambiguity. Designers get enforced systems that reduce iteration cycles. QA catches bugs earlier through mutation testing. The compounding effect is the real story: after 3 months the system knows your codebase, your conventions, your users, and what "good" looks like. Each cycle is measurably better than the last.

Are you replacing developers?

No. Human augmentation, not replacement. Designers do more design. Engineers build faster and with better quality. PMs make better decisions grounded in evidence instead of gut feel. Nobody loses their job. The system makes your existing team dramatically more effective by handling the mechanical work so humans focus on judgment, strategy, and the creative decisions that actually matter.

How do I get started with Astrohive?

Book a technical conversation at astrohive.ai/contact. We'll discuss your current stack, pain points, and where agents could add value. If there's a fit, we start with an FDE pilot where a technical person from our team embeds with yours. The system starts by observing, not acting, so there's zero risk on day one. Value compounds as agents earn trust and you see results.

Why should I trust a newer company with my engineering workflow?

Three reasons. First, our architecture is designed so agents can't do damage: they start as observers, earn trust incrementally, and operate in sandboxed environments with budget limits. Zero risk on day one. Second, the founding team built 200+ software products over 10+ years, so the agents are trained on real patterns from real products, not theoretical best practices. Third, you get an FDE who embeds with your team and is accountable for results. You're not buying software and hoping it works. You're getting a technical partner who configures the system, monitors outcomes, and iterates until it delivers. We're not asking for blind trust. We're asking for a chance to earn it, which is exactly how our agents work too.

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Currently in early access with enterprise clients.

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