Accuracy gets an agent approved. Evidence gets it deployed.
Who authorized this, what did it touch, what did it cost, and can you prove it six months from now. InstaControl™ answers all four, for every action, before it runs and after.
Agent
- Runquote.reprice.v3
- Purposerepricing
InstaControl™
- Accessrole + purposebound
- Policymargin_floor.v4allow
- Approval24% over 20%held
- Cost$0.11 · 18.4k tok
ERP
- Writeerp.updateQuote
- Undoidempotency_key
Your Postgres
- Trace6 links · 0 gaps
- Outcomemargin 31 → 38%
Three layers. You control the top one.
Knowledge underneath, execution in the middle, your controls on top. The same three layers whether the work is quoting, dispatch, or field service.
Policy is configuration, not a release. Your team changes what agents may do without filing a ticket, and without waiting for us.
- Control and auditInstaControl™Who may act, what it costs, what it did.You control this
- ExecutionInstaWorkers™The agents and workflows that do the work.
- Knowledge and memoryInstaBrain™What the business knows, kept current.
Deciding and proving are the same surface.
Most teams buy these separately and find the gap in the incident review: a policy tool that cannot see runtime, and a dashboard that can see everything and stop nothing.
Control before the action
Evidence during and after
- Policyruntime, not prompts
- Accessroles and purpose
- Approvalroute and timeout
- Budgetcaps per capability
- Reversibilityrollback handler
- erp.updateQuoteThe actionallowed, held, or declined
- Tracehash-chained steps
- Costtokens and latency
- Evalson your datasets
- Outcomerevenue and SLA
- Audityour Postgres
What one request writes to your database.
Agent spend is not a monthly invoice surprise. The gateway wraps the model call itself, so cost and latency belong to the request, the session, the user, and the workflow that caused them.
- Ships as / GatewayA Python package that wraps your LLM calls and writes telemetry to a Postgres you already run.
- Ships as / InfrastructureA Terraform stack for dashboards, alerting and retention. Or skip it and use your own.
- Context412ms
- Model1020ms · ttft 240ms
- Tool call1200ms
- Cost
input_tokensoutput_tokensusd_costsession_cost - Latency
time_to_first_tokentime_to_last_tokentotal_duration - Config
model_idtemperaturetop_ptop_kmax_retries - Identity
projectenvironmentuser_idsession_idevent_id - Tools
tool_namearguments_classruntime_ms - Payload
request_sizeresponse_size
OpenAI · Anthropic · Google, one schema, your database
It runs on your traffic before it runs on your customers.
Promotion is a decision someone on your side makes, against numbers, on the record.
- ShadowReal traffic copied in. Nothing exposed.
- CanaryFirst live outputs. Acceptance rate starts counting.
- ExpansionHeld against accuracy, latency, cost, review rate.
- StandingFull traffic, still gated on every release.
The questions your team asks in the first hour.
Single-tenant, in your environment, with isolation enforced at the storage layer rather than by an application filter someone can forget.
Your environment · cloud, on premises, or air-gapped
InstaControl™
- PolicyRuntime middleware
- ApprovalsRoute + timeout
- TraceHash-chained
Identity
- SSOYour IdP
- ScopeRole · attr · purpose
Secrets
- StoreVault / OpenBao
- KeysNever held by agents
Agents
- OursInstaWorkers™
- YoursVia the gateway
Your Postgres
- RecordTraces · costs · approvals · outcomes
- ExportSplunk · Datadog · your SIEM
Model providersoutside the boundary, and optional. Nothing in the design assumes an outbound connection to us.
- Is our data training a shared model?
- No pooling, no cross-customer training. Your data trains your models only, and the knowledge stays yours if you leave.
- Does this replace our observability stack?
- No. It captures the layer your APM cannot see, and exports downstream to the tools you already page on.
- Does it work with agents we built ourselves?
- Yes. The gateway wraps model calls from your own code, and the plane covers any action registered against it.
- What happens when an agent gets it wrong?
- The action was reversible by contract, the approver who let it through is named, and the trace shows what it reasoned over.
The plane is the part that does not get replaced.
The model, the provider, and the machine underneath will each change several times inside the life of one deployment.
The control layer has to survive that, or you re-approve your AI program every time the stack moves.
Stays put
- Who may act
- What needs sign-off
- What it cost
- The trace
- The eval gate
- The audit record
- Frontier model
- Provider and price
- Cloud, on-prem, edge
- Large or distilled
- Which workers
- Workflow definitions
Expected to change
- Shipping
Authoring moves to your side
Policy editing, approval design, and a no-code builder your operators can read without an engineer in the room.
- Next
A control worker, not just a console
Agents that audit, scan for compliance drift, and validate policy continuously, instead of a quarterly review that samples.
- Next
The same plane at the edge
An air-gapped rack gets the same policy surface and the same audit record as your VPC, with no raw data leaving the site.
- Next
Replay, not reconstruct
Step back through a live episode exactly as it ran, and approve from voice or mobile when it cannot wait.
What your team gets to touch, and when.
A filled dot is running in deployments now. Ask us where a roadmap item stands before you build a plan around it.
12 live12 next
Live todayNext
- Cost, latency, attribution5 of 8 live
- Per-request cost and tokens
- TTFT, TTLT, total duration
- Every tool call and its runtime
- Model config per call
- Cost by project, env, user, session
- Spend caps
- Approved-model catalog
- Outcome attribution
- Traces, records, memory1 of 4 live
- Telemetry in your Postgres, SIEM export
- Trace viewer with hash chain
- Memory and knowledge controls
- Episode replay
- Policy and access0 of 3 live
- Nothing yet
- Policy editor
- Role and attribute access, purpose binding
- No-code workflow builder
- Approvals and reversibility1 of 3 live
- Approvals inside deployed workflows
- Approval design: predicate, route, timeout
- Checkpoints and rewind
- Evals and release3 of 4 live
- Continuous evals on your datasets
- Release gates that roll back
- Shadow traffic and canary
- Retirement console
- Deployment and ops2 of 2 live
- Single-tenant cloud, on-prem, air-gapped
- Prometheus, Grafana, Alertmanager
- Complete
The short answers.
1Is this LLM observability with a new label?
Observability tells you what happened. It cannot decline an action. InstaControl™ evaluates policy before the tool call, holds the action for an approver when a threshold is crossed, and then records both facts in the same trace. The dashboard is one half of the product.
2We already have SOC 2 controls and change management. Why another plane?
Because your existing controls assume software behaves the same way twice. Agent behavior emerges at runtime, so the source of truth moves from the code to the trace. This is the plane that makes runtime behavior reviewable with the process discipline you already run.
3How much of this is on us to implement?
Instrumentation is a package install and a database connection. The policies, thresholds, and approval routes are decisions only your team can make, and we facilitate them during deployment rather than shipping defaults you would have to discover.
4Can we run it without egress?
Yes. The observability stack is built on components that run in your environment, and the design excludes anything that cannot operate air-gapped at the edge.
5Who can see a trace?
Whoever your access rules say. Traces are subject to the same role, attribute, and purpose rules as the data inside them, and views are derived from the underlying chain rather than assembled by hand.
6You will change models and providers. What breaks on our side?
Nothing you authored. Routing decides which model and which environment suits a task; your policies, thresholds, and approval routes sit above that as configuration rather than code. The evals are how you verify that a swap, including a swap to a smaller and cheaper model, did not quietly cost you accuracy.
7What do we own at the end of the contract?
The audit history, the policies, the evaluation datasets, and the knowledge your corrections built. It is in your database, in your environment.
Behind this solution: Lily, the AI Forward Deployed Engineer, working on the platform Lily builds on, delivered through forward deployed engineering. InstaControl™ sits over what the platform executes and what InstaBrain™ knows, including the workflows behind InstaProspect and InstaQuote, and it runs on the Small Data Center where the edge requires it.