Why Amodal
The thesis behind the product. Why configuration beats intelligence, why the runtime is free, and why this pattern keeps repeating.
Configuration is orthogonal to intelligence.
Models get smarter every quarter. But even a model that knows everything still needs to be told what to do, how to do it, what it's allowed to do, and what your specific business looks like. That's not intelligence. That's configuration. And it gets more valuable as models improve.
Your methodology (Skills)
"How we triage deals at Acme" isn't in any training data. Your sales cycle is 45 days for mid-market, 90 for enterprise. Deals go stale at different rates per stage. Enterprise deals get 2x thresholds. None of this is something a model learns. You configure it.
Your system access (Connections)
Claude knowing the Salesforce API schema doesn't mean it has your OAuth tokens. It doesn't know which 5 of 47 endpoints matter to your sales team. It doesn't know your instance URL, your custom fields, or your rate limit quota.
Your private data (Knowledge)
Your baselines ("normal login volume is 2,000-3,000/day"). Your patterns ("this vendor always resubmits invoices late in Q4"). Your false positives ("the 2am activity in Zone B is scheduled maintenance"). Private. Specific. Not in the training data.
"Deals silent 14+ days in Negotiation close at 8%"
Source: 47 sessions over 3 weeks
"Acme vendor resubmits on the 15th, not a duplicate"
Flagged 3x, resolved identically
Your guardrails (Rules & Access)
Field-level access control: the agent reads opportunity amounts but never exposes margin data below VP. Confirmation tiers: writes require human review. Rate limits. PII handling. A smarter model doesn't make guardrails less important. It makes them more important.
Your presentation (Output & Brand)
Which widgets render. What language to use. What the agent's personality is. What gets emphasized vs buried. This is UX and brand. It changes per customer, per channel, per audience.
Smarter models need more configuration, not less.
The assumption is that as models get smarter, you need less tooling around them. The opposite is true.
More capabilities = more to govern.
A model that can only read needs simple guardrails. A model that can read, write, delete, send emails, create Jira tickets, and update CRM records needs a comprehensive policy layer. Every new capability is a new surface area for misconfiguration.
More intelligence = more context required.
A smarter model can do more with the right context. But "the right context" is your specific sales process, your specific API endpoints, your specific compliance requirements. The smarter the model, the more valuable precise configuration becomes.
More adoption = more diversity of use.
When every team in the company has an agent, the configuration surface explodes. Sales ops wants deal triage with their pipeline stages. Finance wants reconciliation with their chart of accounts. HR wants onboarding with their specific policies. Same runtime, different configuration.
Rules are the new code.
A skill is a methodology file. Your sales playbook, your investigation runbook, your compliance checklist. Domain experts author them. Engineers don't need to be involved.
The repo structure IS the product. Skills define what the agent does. Connections define where data lives. Knowledge defines what the agent knows. Evals define how you know it works. All version-controlled. All reviewable in a PR. All testable in CI.
This is the Terraform insight applied to AI: the configuration is more valuable than the runtime. You don't build infrastructure by writing Go code for the AWS API. You write .tf files. You don't build agents by writing Python glue around an LLM. You write .md files.
You bring the domain. We bring the infrastructure.
Your repo contains the domain-specific parts. Everything else is the platform.
Production infrastructure, included.
Everything you need to run AI agents at scale, out of the box.
Cost tracking
Per-session, per-skill, per-tenant cost breakdown. See exactly what every agent run costs. Compare models on your actual workloads. Set budget alerts before you get a surprise bill.
Eval suites
Every deploy runs your eval suite. Skills that regress don't reach production. PRs show pass rates. Compare quality across model swaps before committing.
Session replay
Full transcript of every conversation. What the user asked, which skills fired, what tools were called, what actions were taken, and what the agent said back. Aggregate query patterns across tenants to find what users actually need.
Team and governance
Role-based access. Domain experts edit skills in the browser. Engineers manage connections in git. SSO and audit logging for enterprise. Multi-tenant isolation so each customer's data stays separate.
This pattern has played out before.
Every major platform develops a configuration layer. The core technology provides the engine. The configuration layer is how teams actually use it in production.
What the configuration layer handles.
Model providers focus on intelligence. The configuration layer handles everything around it: how you connect systems, define methodology, manage access, and share across teams.
npm doesn't replace Node.js. It's the layer that makes Node useful in production. Amodal is the same layer for AI agents.
Every ecosystem gets a package manager.
Node got npm. Python got pip. Rust got Cargo. Infrastructure got Terraform providers. AI agents will get one too. We think it should be open.
Connections are the killer content. When Company A publishes a Salesforce connection package with curated endpoints, entity descriptions, and access policies. Company B installs it and customizes only what's specific to them. The catalog compounds.
Terraform went from 20 providers to 3,000+. Each one made the platform more valuable for everyone. We're building the same dynamic for agent configuration.
We want a hundred companies building on us for free.
A hundred companies will build AI agents for their vertical: security, compliance, finance, HR, customer success. If we charge a platform tax on the runtime, they'll evaluate us against building their own. They have VC money. They'll build their own. We get zero.
If the CLI is free forever, they build their agents as Amodal skills. Ship in weeks, not years. Their customers use our skill format. The format becomes the standard. The ecosystem compounds.
We don't make money from the next vertical agent company directly. We make money because they proved our format works for security, which makes the next company choose it for compliance, which makes the next one choose it for finance.
Ready to start building?
Open source. Free to start. The platform scales when you need it.
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