Trusted by AI-first companies.

Build, run, and scale
your agentic workflows.

Define, deploy, and monitor AI workflows with budgets, guardrails, and logs from day one. No bespoke infra required.

1

Define your agent

Describe workflows, tools, routes, and memory in floyd.yaml—your single source of truth.

2

Connect tools & data

Plug in MCP servers (e.g., GitHub), HTTP targets, and vector stores. Secrets stay in our vault.

3

Set guardrails & budgets

Lock down egress domains, enforce output schemas, redact PII, and cap per‑session spend.

4

Deploy & observe

One command to production. Watch traces, tokens, latency, and costs in real time.

Ship agents with confidence.

Declarative workflows

One spec compiles into a managed runtime—routes, memory, retries, and versioning included.

Security & guardrails

Egress allow‑lists, secret scopes, PII redaction, and JSON‑schema output validation by default.

Budgets & cost control

Per‑session spend caps with kill‑switches, alerts, and model routing to keep costs predictable.

First‑class observability

Step‑by‑step traces, token and cost accounting, latency percentiles, and failure triage.

MCP‑compatible tools

Publish/consume tools via the Model Context Protocol; connect GitHub, internal APIs, and more.

Progressive delivery

Canary rollouts, instant rollback, and versioned deploys for safe iteration.

Ready to begin?Agents in production—fast.

Define, deploy, and monitor AI workflows with budgets, guardrails, and logs from day one. No bespoke infra required.