computer use
Operate browsers, terminals, and files
Give agents a real work lane for web apps, command-line tasks, repository changes, and desktop-style workflows while material steps are recorded.
Audit-friendly agents that improve every run.
Every action leaves a receipt, so agent work can be replayed, audited, and improved.
Beetle gives agents a runtime that records how work actually happened. Every tool call, handoff, check, retry, branch, wait state, and control decision becomes a receipt, so finished runs become evidence for better future runs.
Agent Harness
Built for long-running work, with real controls.
Beetle gives agents a governed lane for work that spans browsers, terminals, repositories, cloud systems, SaaS tools, and approved integration actions. They can keep moving across tools, queues, checks, reviews, and policy decisions while every material step stays tied to one receipt record.
coding agents
Orchestrate coding agents end to end
Dispatch coding agents into repo workspaces, track progress, collect checks and handoffs, and fold the result back into the same receipt record.
integrations
Connect agents to the systems they need
Configured provider connectors, CLIs, and scoped actions let agents inspect and operate external systems without becoming ungoverned scripts.
policy
Control what agents can do
Route work through lanes, apply runtime and merge policy, and keep decisions explainable against the exact context available at that moment.
replay
Rebuild the full execution
Replay includes model interaction plus queue lifecycle, leases, heartbeats, retries, commands, tool results, checks, and memory promotion.
improvement
Turn finished runs into better agents
Promote lessons from reviews, failures, checks, and handoffs into memory, policy, and runbooks so the next run starts with better context.
Slack + Web
Start from Slack or the web. Keep one run record.
Mention @beetle in a channel or launch work from the web app. Beetle sends the same runtime through connected systems, approved tools, checks, and decisions, then keeps the receipts for what happened across the run.
1
start
Kick off work from Slack or the web app. Both entry points create the same receipt-backed run record.
2
operate
Agents use connected systems, computer lanes, CLIs, and scoped actions through runtime policy.
3
return
Progress, decisions, and results surface back where the work started while receipts preserve the runtime evidence.
Every material step leaves a receipt.
A receipt is not another chat message. It is the structured record for a runtime event: the objective accepted, the worker leased, the tool called, the policy applied, the check passed, or the memory promoted.
record
Capture material steps, decisions, checks, and context as they happen.
explain
See what changed, what approved it, and what context was available.
improve
Turn failed or messy work into stronger policy, checks, routing, and memory.
beetle receipt
RUN-4B6E-21D9
statusverifiedsourcereceipt streamfoldprojection ready
Objective accepted
OBJ-7F3A-9C2D
Worker leased
Agent lane reserved
Actions executed
Tools, browser, files
Checks passed
Validation attached
Review complete
Candidate promoted
Receipt issued
Replayable chain sealed
1
plan
Objective and policy are captured
The run starts with a receipt for the objective, constraints, selected lane, and policy context.
2
execute
Material steps write receipts
Tool calls, browser work, file changes, queue leases, heartbeats, and runtime commands append to the same chain.
3
verify
Checks become part of the run
Validation, reviews, merge decisions, and candidate status are recorded before the system advances.
4
replay
Replay rebuilds the harness state
Beetle can reconstruct what the agent saw, what the runtime did, and why the next decision was allowed.
Runtime Replay
Watch the run rebuild itself.
Replay is not a video of the agent. It is Beetle rebuilding the objective, actions, checks, policy decisions, and memory updates from the receipt chain.
replay receipt
OBJ-7F3A-9C2D
statuspolicy loadedchainappend / fold / replay
The objective, lane, constraints, and allowed systems are captured before work begins.
active fold
Objective accepted
Runtime
Long-running agents need more than a chat transcript.
Chat history cannot explain queue state, retries, leases, policy gates, branches, reviews, or memory promotion. Beetle treats the run itself as the source of truth, so runtime state can be reconstructed instead of guessed.
append
Material steps are written first
Tool results, human decisions, checks, queue state, and merge decisions are recorded before the run moves on.
fold
State is derived, not hidden
Views, traces, queues, and UI models are pure projections over the run record. The cache can disappear; the chain remains.
orchestrate
Long-lived work stays coordinated
Beetle can dispatch workers, lease jobs, collect checks, review candidates, and promote work without a second source of truth.
replay
Replay explains runtime decisions
A run can be reconstructed from the receipt chain: model interaction, action selection, queue movement, policy decisions, and why control moved forward.
Integrity
The integrity model behind every run.
Receipt events are typed, immutable, and linked into durable streams for objectives, jobs, agent actions, reviews, checks, memory, and runtime control. That gives Beetle a reliable record for replay, audit, and promotion decisions.
Hash-linked integrity
Receipts are canonical, previous-hash linked, and guarded against unexpected writes.
Typed event streams
Agent runs, jobs, memory, sub-runs, and objectives are organized into durable stream families.
Control decisions are events
Leases, heartbeats, queue commands, policy choices, merges, reviews, and promotions are captured in the same model as domain work.
Auditing
Replay failures into better operations.
When an agent drifts, retries, misses context, or passes a weak check, Beetle keeps the full receipt chain available for review. Teams can find the failure point and update policy, validation, routing, runbooks, or memory before the next attempt.
1
Replay the exact step chain
2
Find weak checks, bad routing, and missing context
3
Update policy, validation, memory, or runbooks
4
Start the next run with the fix already in place