Execution Reliability
Infrastructure For AI Agents

Run real tools without retry glue, duplicate actions, or stale approvals.

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$ npx invoke democopy

gateway.invoke.dev

live · 99.99%

Agentclaude · gpt · custom

Invokehosted gateway

Policypolicy-as-code

Approvalhuman · slack

Tool / APIgithub · notion

approval pending

Recent callslast 60s

github.list_repos47ms · scope:read
github.create_issueawaiting @ops in #approvals
notion.search120ms · scope:read
stripe.create_chargepolicy:high_risk · denied

Connects to the tools your agents already use

GitHubNotionLinearSlackOpenAPIMCP

Plain English

Invoke helps AI agents do real work without making a mess.

Your agent still decides what to do. Invoke makes sure the action is scoped, checked, traceable, and safe to retry before it touches production systems.

Agents move faster

Developers stop rebuilding retry, auth, approval, and logging code for every tool.

Mistakes stay contained

When a payment, CRM update, or message looks risky, Invoke checks state before the action lands.

Teams see what happened

Every execution gets a trace, so product, ops, and engineering can review the same story.

Agent

Wants to act

Refund, update CRM, post to Slack, create a ticket.

Invoke

Controls execution

Checks scope, state, retry rules, approvals, and idempotency.

Tool

Runs with guardrails

The side effect lands once, or gets blocked with a trace.

Interactive execution

Watch an agent hit production failure modes without touching production.

The chat is what your agent wants. The rail is what Invoke controls: classify, reconcile, verify, and trace every side effect.

Agent execution thread

refund_guardrail / crm_update

live simulation

RevOps agent

Refund Acme after a webhook timeout, then update the CRM account.

Runtime control rail

Side effects move through Invoke first.

Tool classified

Stripe refund is unknown-effect. CRM update is entity-sensitive.

Live state reconciled

The refund already happened, so retry becomes do_not_retry.

Customer verified

Resolved customer and write target do not match.

Execution proof returned

The agent gets a clean result plus the trace operators need.

Refunds

1, not 2

CRM writes

0 wrong

Trace

ready

Stripe + CRM execution run

Tell your agent what to do.Invoke controls the execution.

Run a messy real-world action and watch Invoke prevent duplicate charges, block wrong CRM writes, and leave the trace.

invoke.execute("payment_collection")

Ready to run with control.

The agent will try to charge Stripe and update CRM. Invoke sits in the execution path before the side effects get messy.

Visual story

Think of Invoke as the checkpoint between an agent and the real world.

The agent asks for an outcome. Invoke turns the action into a controlled execution: check what changed, prevent duplicates, pause risky work, and show the trace.

Reconcile before retry

Verify live state

Prevent duplicate effects

Return execution proof

Agent execution thread

refund_guardrail

live / refund

Agent

Refund this customer after the webhook timeout.

Invoke

Checking payment state, customer identity, and webhook delivery first.

Invoke

Wrong customer detected. Refund blocked. Trace recorded.

Money moved

Customer

verified

Trace

ready

Execution layer

One place to see what your agents are about to touch.

Invoke turns live tool calls into a controlled workspace: every action has scope, state, policy, outcome, and trace before it reaches production.

Invoke

ExecutionsPoliciesTraces

Agent

Invoke

CRM

Linear

Slack

Controlled result

Wrong CRM update blocked

Entity mismatch caught before the customer record was touched.

Approval checkpoint

Paused until live state matches policy.

Use cases

Built for teams already letting agents touch real systems.

These are the workflows we are validating with early teams. Actual customer stories will replace this section as pilots move into production.

Looking for 5-10 design partners

AI support agents

Refunds, credits, escalations, and customer updates

Tangible benefit

Stop wrong-customer actions and duplicate retries before they touch billing or CRM.

Fintech and revenue ops agents

Payment collection, webhook recovery, invoice follow-up

Tangible benefit

Reconcile unknown outcomes so a timeout does not become a duplicate charge.

Developer tooling agents

Linear/GitHub issues, Slack release posts, incident follow-up

Tangible benefit

Keep engineering queues clean with idempotency, approval gates, and traces.

The problem

Agents can think. Production breaks when they execute.

The hard part is no longer just choosing the next step. It is making sure the step actually happens once, against fresh state, with a trace your team can inspect.

Agents fail after the decision

The model may choose the right action, then the tool times out, partially succeeds, or returns an error the agent cannot safely interpret.

Multiple agents multiply the mess

More agents means more retries, collisions, stale assumptions, duplicate tickets, duplicate messages, and conflicting actions on the same resource.

Frameworks stop at the tool call

Agent frameworks help with reasoning. Connectors help with access. Neither gives teams the operational control layer every production action needs.

Failure landscape

Where agent execution gets expensive

real production failure modes

Failure	Damage	Execution control
Auth expired	Workflow fails or loops on bad credentials	Classify, stop unsafe retry, escalate
Tool timeout	Unknown whether the action happened	Mark unknown outcome, reconcile first
Partial execution	State becomes inconsistent	Refetch live state and reconcile
Duplicate retry	Duplicate tickets, messages, charges	Idempotency and replay protection
Stale approval	Approved action runs after state changed	Freeze, thaw, revalidate, requeue
Webhook drift	Agent acts on an event that did not land	Verify against source-of-truth state

The result

Agents teams can actually trust.

The same infrastructure your platform team would build given six months — without the six months.

approvals · queue

3 pending

high riskgithub.create_pull_request
acme/api · main ← agent/fix-flake
@deploy-bot 12sslack
medium risklinear.update_issue
ENG-2241 · status → done
@ops-copilot 1mdashboard
high riskstripe.refund_charge
ch_3PqL · $429.00
@support-agent 3mslack

approval queue

Pause risky actions. Resume them with one click.

Every flagged call lands in the approver's queue with full context — the agent, the action, the resource, the policy that triggered it. Approvers respond in Slack, email, or the dashboard. Agents pick up exactly where they left off.

Outcomes

Developers connect tools faster
One SDK call replaces a directory of OAuth flows, retries, and tool wrappers.
Operators approve in Slack
Risky actions hit the channel they already live in — or the dashboard, or email.
Security gets policy-as-code
Scoped credentials, deny-by-default, and exportable traces for every call.
Every action is observable
Reviewable, replayable, and stitched into LangSmith and Helicone traces.

agent · deploy-bot · scopes

least privilege

githubissues:writeallow
githubdeploys:triggerapproval
notionpages:readallow
linearissues:writeallow
stripecharges:createdeny

audit · last 6 events

ts	agent	action	verdict	latency
14:02:11	deploy-bot	github.create_pr	allow	184ms
14:02:09	ops-copilot	linear.update_issue	allow	92ms
14:01:58	support-agent	stripe.refund_charge	approve	21s
14:01:43	deploy-bot	github.merge_pr	deny	8ms
14:01:30	ops-copilot	notion.search	allow	120ms
14:01:21	support-agent	slack.post_message	allow	61ms

How it works

One gateway between agents and the tools they use.

A single hop that knows what your agent is allowed to do, what needs human review, and what to record. Drop-in for any agent runtime.

Discover

Agents discover available tools and capabilities through a single, typed catalog — pulled from MCP servers, OpenAPI specs, or first-party SDKs.

Authenticate

Invoke handles scoped credentials and tool-level access. Tokens never touch the agent. Per-agent, per-tool, per-action permissions.

Govern

Policy-as-code decides whether an action runs, needs approval, or should be denied. Versioned, testable, and identical in dev and prod.

Audit

Every call, approval, rejection, and execution result is logged for review and export to LangSmith, Helicone, S3, or your warehouse.

Call any tool

tsagent.ts

1import { invoke } from "invoke"
2
3await invoke.call("github.create_issue", {
4  repo: "acme/api",
5  title: "Fix flaky deploy",
6})

Govern with policy

policyrisk.policy

1# policies/risk.policy
2when action.risk == "high":
3  require_approval(channel="slack", ttl="15m")
4
5when action.tool == "stripe" and env == "prod":
6  deny(reason="manual review only")

Built for production agents.

v0.6 · stable

Policy-as-code approvals

Versioned rules. Testable in CI. Deploy with the rest of your infra.

Freeze / thaw checkpoints

Pause an agent run on a risky action and resume it after a human signs off.

Scoped API keys

Per-tool, per-action, per-environment. No shared secrets, no static tokens.

Hosted gateway URLs

A single endpoint per agent. Region-pinned, rate-limited, observable.

MCP, OpenAPI & first-party

Connect GitHub, Notion, Linear, Slack, or any MCP server in one declaration.

LangSmith & Helicone exports

Stream traces into the tools your team already uses for evals and debugging.

Get started in seconds

Choose your role and package manager to get the command.

npx @invoke/cli

Early access

Try Invoke on one real workflow.

Drop your email. We'll send the fastest path to test Invoke against an agent workflow you already care about.

Email only. We will ask about your workflow after you reply.

Execution ReliabilityInfrastructure For AI Agents

Invoke helps AI agents do real work without making a mess.

Agents move faster

Mistakes stay contained

Teams see what happened

Wants to act

Controls execution

Runs with guardrails

Watch an agent hit production failure modes without touching production.

Agent execution thread

Side effects move through Invoke first.

Tool classified

Live state reconciled

Customer verified

Execution proof returned

Tell your agent what to do.Invoke controls the execution.

Ready to run with control.

Think of Invoke as the checkpoint between an agent and the real world.

Agent execution thread

One place to see what your agents are about to touch.

Wrong CRM update blocked

Built for teams already letting agents touch real systems.

AI support agents

Fintech and revenue ops agents

Developer tooling agents

Agents can think. Production breaks when they execute.

Agents fail after the decision

Multiple agents multiply the mess

Frameworks stop at the tool call

Where agent execution gets expensive

Agents teams can actually trust.

Pause risky actions. Resume them with one click.

Developers connect tools faster

Operators approve in Slack

Security gets policy-as-code

Every action is observable

One gateway between agents and the tools they use.

Discover

Authenticate

Govern

Audit

Built for production agents.

Policy-as-code approvals

Freeze / thaw checkpoints

Scoped API keys

Hosted gateway URLs

MCP, OpenAPI & first-party

LangSmith & Helicone exports

Get started in seconds

Try Invoke on one real workflow.

Execution Reliability
Infrastructure For AI Agents