The JVM for agents.
Build it once. Run it anywhere.
Kapi turns your agent into one release with one fingerprint. The Manifest Engine runs that exact release on-prem, on Azure, AWS or GCP, against your own data, models and keys.
Free to build and deploy. $29/mo when you need two live versions at once.
Infinite experimentation
Nobody knows the right formula for an agent yet. Find it by trying.
- A variant is a copy with one decision changed: the model, the graph, a prompt, where a person approves
- It deploys on the spot, and its scores, cost and speed belong to exactly that variant
- Cost is weighed against quality, so the winner is one you can afford to run
- The people who know the work run the experiments, not only developers
Run it anywhere
Keep the winner as one release with one fingerprint, and run it where you choose.
- The Manifest Engine runs it on-prem, on Azure, AWS or GCP
- It rebuilds the release and must reach the same fingerprint, or it refuses to run
- Your Postgres, your model endpoint, your search, your keys
- The release carries none of your data
Enterprise best practices, built in
What usually gets bolted on months later is there from the first run.
- Every read and write scoped to a customer, a product and a person
- Risky actions wait for a person, and the decision is kept with who made it and why
- Hard limits on spend, steps and tool risk, checked on every run
- A receipt for every answer: the release, the models, the sources, the tools
reach production
will fail by 2027
never deployed
Sources: Deloitte Tech Trends 2026, Gartner 2025
Billions of tokens burned.
Zero production agents.
Your team has had Claude Code, Cursor, and Copilot for a year. So why isn't your enterprise on fire with agentic AI? Maybe just prompting Claude is not going to magically produce a production-ready agent?
The uncomfortable truth
Your AI code editor builds fast. It only builds Layer 1.
So what's missing?
Agentic AI requires a whole new way of thinking about software. Just calling an LLM API is putting lipstick on a pig. Real agents need 8 interconnected layers — and every Kapi blueprint ships all of them.
Derived from analysis of 9 platforms + cognitive architectures (SOAR, ACT-R, BDI). Read the research →
Your agent's first Monday
in production.
This is what happens when you ship an agent with only layers 1 and 2.
Agent hallucinates a revenue figure to the CFO.
Layer 7: Eval — missingCustomer asks a follow-up. Agent has zero memory of the conversation 10 minutes ago.
Layer 5: Memory — missingAgent auto-sends a contract to a client without legal review.
Layer 6: HITL — missingCTO asks "how much is this costing us?" Nobody knows.
Layer 8: Observability — missingAgent pulled offline. Back to square one.
Agent generates a revenue summary. Eval layer catches low-confidence figure, flags it for human review before sending.
Layer 7: Eval — activeCustomer follows up. Agent recalls the full conversation, references their previous preferences.
Layer 5: Memory — activeContract ready. HITL gate routes it to legal for approval before delivery.
Layer 6: HITL — activeCTO opens dashboard. Sees cost per run, latency per step, and quality scores — in real time.
Layer 8: Observability — activeAgent has handled 47 tasks. Zero incidents. Still running.
Each layer a product uses is decided in one manifest, built into one release.
Your first version in 5 minutes.
Not a toy. A solid one.
You wouldn't build a home without blueprints. Same here — tested, with every layer decided in one manifest. Configure, deploy, iterate.
Convinced? Jump into a live blueprint.
Start customizing in under 5 minutes. No credit card required.
Wrappers demo well.
They don't deploy well.
Dify, CrewAI, n8n — they stop at Layer 3 and call it done. No evals, no HITL, no observability. That's why their agents don't survive production.
| Feature | Kapi | Dify | CrewAI | n8n | Gumloop |
|---|---|---|---|---|---|
| Visual Canvas | — | ||||
| Manifest + Data Export | — | — | — | ||
| Built-in HITL Queues | — | — | — | — | |
| Eval Framework | — | — | — | — | |
| Spec-Driven Dev | — | — | — | — | |
| Cost Tracking | — | — | — | ||
| Multi-Agent | — | ||||
| 150+ Integrations | — | — | |||
| PM-Friendly UI | — | — |
100 agents your competitors
haven't built yet.
While your industry debates AI strategy, you could have 10 agents in production across HR, Finance, and Legal — by Friday.
reduction in resume screening time
Human Resources
- Screen resumes and rank candidates with semantic matching.
- Answer benefits and policy questions from the company handbook.
- Automate onboarding — accounts, orientation, welcome packets.
- Analyze exit interviews to surface retention insights.
straight-through invoice processing
Finance & Accounting
- Extract invoice data, match to POs, route for approval.
- Audit expenses against policy and flag violations.
- Forecast cash flow from AR/AP and seasonal patterns.
- Detect fraud in real-time with anomaly monitoring.
faster contract review cycles
Legal & Compliance
- Review contracts, extract key terms, flag risky clauses.
- Monitor regulatory changes and map to business operations.
- Process GDPR data subject requests in 48 hours vs. 30 days.
- Score litigation risk with precedent analysis.
See inside a blueprint.
Every component is visible, configurable, and exportable. No black boxes.
100+ blueprints across 8 categories — or build your own from scratch
From the Blog
Insights on building reliable AI agents.
Stop building demos that die.
Build agents that survive Monday morning. Iterate in minutes. Your manifest and data are yours — export anytime.