If you’re comparing us to “AI in your IDE” tools: those run one agent at a time on your
laptop. AgentBees runs N, in parallel, on your cloud, with team-wide review + audit +
billing.
The five differentiators
Parallel, not serial
Fan out one task to N agents/models in isolated workspaces, compare diffs, ship the winner. This
is the wedge — every other “AI IDE” runs one agent at a time.
Any CLI agent, one control plane
Claude, Codex, Gemini, Grok, Kiro — same task surface, same diff/PR flow, same billing, same
audit trail.
Fast, disposable workspaces
Sub-second pod boot, one container per agent, per-task checkpoints + point-in-time restore. A
task, not a laptop, is the unit of work.
Enterprise-safe by construction
Internal AI gateway (org creds, no per-user keys), OrgPolicy + zero-retention, SSO-brokered
credentials, tenant isolation, per-tenant cost attribution & chargeback.
HoneyBox skills + shareable runs
Reusable, org-scoped agent playbooks (auto-inject or on-demand) plus opt-in
/share read-only
links, so wins compound across the team instead of living in one person’s shell.Beyond a single task: multi-agent pipelines
A single task is just the start. Compose a pipeline of bees —jira → design → dev → QE → PR → security — with human gates between stages,
a verdict-gated remediation loop (max 3 rounds) when QE or security fails,
and per-lane fan-out for AIDLC-style breakdowns. This is where most competitors
stop at “one agent, one task.”
See Pipelines for how to compose one and
Flow for the mission-control view of every run in your org.
Runs where your code lives
- AWS EKS today — shared pool namespace, dedicated per-tenant namespace, or dedicated cluster SKUs.
- GCP + Azure on the roadmap.
- BYO-compute runner on the backlog for orgs that want agents inside their own VPC — the platform ships the runner, they host it.
The pitch grid
Next
FAQ
The isolation / tenancy / credentials / pricing questions serious buyers ask.
Onboarding
Sign in, connect a repo, run your first agent.