AI agents, shipped like infrastructure.
Bandito AI — governing AI systems that don't play by the rules.
Bandito AI is a declarative control plane for AI systems. Define agents as code, diff changes, promote dev to prod, and stay audit-ready.
Reproducibility
Pin models, tools, prompts
Diffable changes
git diff for AI behavior
Durable state
Resume, replay, and audit runs
Observability
Trace every run, tool call, outcome
Portability
Swap models and runtimes cleanly
Governance
Policies, audit trails, rollbacks
version: 1
project: bandito-demo
providers:
openai:
type: openai
model: gpt-4.1
agents:
support_bot:
provider: openai
model: gpt-4.1
temperature: 0.2
system_prompt: "You are a helpful support agent."
guardrails:
pii: true
jailbreak_protection: true
deployment:
environment: prod
AI Agents as Code, done with discipline.
The problem
AI systems are shipped like demos.
Teams hack together agents with prompt glue, hidden configs, and ad-hoc scripts. When behavior changes, nobody can explain why.
Drift and chaos
Same agent behaves differently across environments.
No diff, no rollback
Changes land without review or traceability.
Shadow AI
Tools and data paths are scattered and ungoverned.
Compliance pressure
Audit questions arrive after production incidents.
Important insight
Everyone is building pieces. No one owns the control plane.
Prompts, chains, graphs, evaluations, observability. The missing layer is declarative deployment and governance.
Pieces, everywhere
Prompts, chains, graphs, evaluations, observability.
Positioning
Declarative deployment and governance for AI agents, independent of runtime.
Clear, practical, and anchored in today's pain.
The solution
Bandito makes AI behave like infrastructure.
Define agents declaratively, compile to your runtime, and ship with the same rigor you use for Terraform, Helm, or GitOps.
- Declarative agent specs
- Environment promotion: dev -> staging -> prod
- Provider-neutral control plane with policy + audit
- Durable agent state for recovery + replay
- Bandito Cloud for Agents-as-a-Service (AaaS)
Plan
Preview changes before they hit production.
Apply
Write state, lock versions, and deploy safely.
Run
Execute agents with reproducible configs.
Govern
Guardrails, PII policy, and audit trails.
How it works
Three steps to infra-grade agents.
Define
Write bandito.yaml with model, tools, memory, and policy.
Plan
See a diff of what changes and why.
Apply and run
Promote environments and execute safely.
bandito init
bandito validate
bandito plan
bandito apply
bandito run
Evaluations
Quality gates before anything hits prod.
Bandito ships eval suites with every agent. Run smoke and regression checks during plan/apply, and block deployments that fail safety or behavior thresholds.
- Golden prompts with expected outcomes
- Pass/fail thresholds + safety assertions
- Model-locked evals to prevent drift
- Eval history and diffable score changes
agents:
support_bot:
evaluations:
- name: smoke
required: true
cases:
- input: "Reset my password"
expect_contains: "reset link"
- input: "My SSN is 123-45-6789"
expect_not_contains: "SSN"
- name: regression
required: true
pass_threshold: 0.9Stack
Bring your runtime. Bandito stays neutral.
Compile into LangChain or LangGraph, plug in your vector DBs and data sources, keep policy centralized, or run on Bandito Cloud.
Ideal users
Built for platform and AI infra teams.
If your org has more than one agent in production, Bandito keeps them aligned, auditable, and repeatable.
Platform teams
Standardize deployment, guardrails, and ownership.
AI architects
Define the control plane before sprawl sets in.
Security and compliance
Audit-ready logs and policy enforcement.
Bandito AI is seeking design partners.
Join the first cohort to shape the spec, influence integrations, and lock in early access.