DETERMINISTIC AI EXECUTION

Control your AI execution.

Run AI workflows inside your repository with replay, trace, and approval control. ExecInspector turns opaque automation into a reviewable, policy-aligned execution system.

Same input -> Same graph -> Same tools -> Same events -> Same outcome

If an AI touches your code, you should be able to replay it.

Run your first task See how it works
Private install No hosted login is required in the current phase.
Replayable Execution history is inspectable through events, trace, and replay.
Pilot-first Sold as one repo, one team, one deterministic workflow to start.
STATE events + snapshots + queue
ENTRY one repo, one owner, one workflow
OUTPUT replay, audit, operating discipline

EXECUTION LEDGER

A workflow should read like an operating record, not like a black box.

RECORDED RUN

TRACE tr_2026_0416_001 team-pilot / auth-review-path
STATE queued -> running -> reviewed single declared path
TOOLS read_file | git_diff | pytest contract-based execution only
EVENTS task_created | node_started | node_completed append-only review trail
RESULT qualified for replay and audit same path can be reviewed again

WHY THIS MATTERS

  • Execution becomes evidence instead of opinion.
  • Operators stop improvising hidden state in parallel tools.
  • Reviewers can ask what happened and get an actual record.
  • Management gets a narrower, more defensible first deployment.

PRODUCT VIEW

The product should show state, event flow, and control boundaries in one screen.

trace tr_2026_0416_001 replay eligible reviewed output

RUN STATUS

Current node validate_output
Task state reviewed
Approval mode operator gated

EVENT STREAM

09:41:03task_createdworkflow locked to auth-review-path
09:41:05node_startedanalyze_repo
09:41:08node_completeddependency graph extracted
09:41:12tool_calledgit_diff
09:41:19review_readyoutput preserved for replay

CONTROL SURFACE

  • Declared tools only
  • Event-backed state transitions
  • Explicit review checkpoints
  • Replay path available after execution

RUNTIME SURFACES

The product is intentionally narrow. Each surface exists to remove execution ambiguity.

EVENT STORE

Append-only execution record

Every task path emits durable events instead of hiding state inside operator memory.

TASK GRAPH

Known dependency shape

Tasks resolve into declared nodes and transitions, not free-form branching behavior.

QUEUE + WORKER

Operational separation

Scheduling and execution are separated so the workflow can be retried, reviewed, and extended safely.

INBOX + CRM

Commercial intake stays inside the product surface

Lead capture, qualification, dashboard, and export now run inside the same private footprint.

THE PROBLEM

AI breaks silently. Until it does not.

RISK 01

Execution variance

The same task produces different paths and different quality depending on the tool, operator, or session.

RISK 02

No replay

When a workflow fails, teams often cannot reconstruct exactly how the AI-assisted path ran.

RISK 03

Audit gaps

Tool choice, hidden state, and side effects make review and approval hard to trust.

RISK 04

Operational drift

Project rules and decision boundaries stay trapped inside people, not in a durable execution surface.

Most AI tooling is stateless and opaque. That is acceptable for demos. It is not acceptable when a team needs to inspect, replay, and explain what happened inside a real repository.

HOW IT WORKS

Every action is forced through one declared execution path.

EXECUTION PIPELINE

Task -> Graph -> Queue -> Worker -> Event Log -> Replay

Every action becomes an event. Every event can be replayed. Every execution can be inspected after the fact instead of guessed from partial outputs.

DEMO FLOW

  1. Submit a task.
  2. Worker executes nodes.
  3. A node fails or requires review.
  4. Replay the execution.
  5. Inspect the full trace and resulting state.

HOW CUSTOMERS USE IT

ExecInspector is delivered as a private install, not as a broad shared-cloud product.

DEPLOYMENT MODEL

  • No public login surface is required in the current phase.
  • The runtime is installed in the customer's repository or private environment.
  • The engineering team keeps its existing AI tools and workflows.
  • ExecInspector standardizes execution around those tools instead of replacing them.

PILOT FLOW

  1. Select one repository and one technical owner.
  2. Define one workflow that must become deterministic and replayable.
  3. Install and align task graph, tools, and policy boundaries.
  4. Run a real workflow and review replay plus audit outputs.

USE CASE

AI-powered PR review is a clean first proof because teams already feel the risk.

PR REVIEW FLOW

  • Agent reviews pull requests against a declared task graph.
  • Risky changes are flagged through typed tools and traceable steps.
  • Suggested fixes remain reviewable before they are trusted.
  • Every decision becomes replayable instead of conversational folklore.

WHY IT SELLS

Teams already ask the same question after AI review: "Why did it say this?" ExecInspector turns that question into a concrete answer with trace, event history, and replay.

STANDARDIZATION GUARANTEES

The product does not standardize agents. It standardizes execution.

Deterministic task intake

Tasks resolve into known execution paths instead of free-form, hidden operator behavior.

Contract-based tools

Actions run through explicit tool contracts rather than arbitrary, opaque command surfaces.

Replay and audit visibility

Teams can inspect what happened, why it happened, and how the same path can be reviewed again.

Private rollout

Initial delivery fits engineering teams that want control over repository access and policy boundaries.

DIFFERENTIATION

Not another AI tool. A stricter execution system.

Typical AI tools ExecInspector
Black-box outputs and session-bound memory Replayable execution with event-backed state
Little or no audit surface Full trace log and reviewable execution history
Opaque tool calling and hidden side effects Declared tools and inspectable control boundaries
Risky automation that is hard to explain Controlled execution that can be replayed and defended

ARCHITECTURE MODEL

A darker rule set: strict in the core, flexible only at the edges.

STRICT CORE

  • Event log is canonical.
  • Task lifecycle is explicit.
  • Worker execution is inspectable.
  • Admin review lives behind session auth.

FLEXIBLE EDGE

  • Private install shape can vary.
  • Tool surface can be aligned to policy.
  • Notifications can stay off until needed.
  • The first package can stay deliberately small.

CLI SURFACE

Simple commands, but the output stays measurable and controlled.

python -m scripts.task submit --goal "analyze PR" python -m scripts.worker run python -m scripts.replay task --task-id 123 python -m scripts.state show

PACKAGE SUMMARY

Three public packages, one hard rule: prove control before scale.

QUICK PILOT

USD 1.5k - 3k

For one repository, one workflow, and one technical owner.

  • Deterministic flow baseline
  • Replay and audit visibility
  • Fast proof inside a real repo

TEAM PILOT

USD 4k - 8k

For one team, one repository, and multiple workflows that need operational consistency.

  • 2-3 deterministic workflows
  • Onboarding and handoff
  • Review rhythm and operating guidance

ENTERPRISE PILOT

USD 8k - 15k

For private install, stricter policy alignment, and stakeholder reporting requirements.

  • Private install planning
  • Stricter execution policy alignment
  • Stakeholder-ready reporting surface
Open pricing details See pilot process

BUYER FAQ

The current model is intentionally narrow, private, and easy to explain.

Does the user need to log into a hosted ExecInspector dashboard?

No. The current phase is private-install and pilot-first. The customer uses ExecInspector inside a repository or private runtime surface, not through a public SaaS login.

Where does ExecInspector run?

In the customer's repository or private environment. The first sale is not a shared cloud control plane.

What does the team get from the first pilot?

One or more deterministic workflows, replay plus audit visibility, and a clearer execution standard for AI-assisted work.

What is not included yet?

No broad multi-tenant admin layer, no shared-cloud rollout claim, and no promise of full production-grade autonomous orchestration.

NEXT STEP

Start with a private pilot, not a platform migration.

The first goal is simple: pick one repository, prove one deterministic workflow, and make replay plus audit visibility usable for the team. Everything else should come after that proof.

Book pilot review Review package ranges

No external booking link is configured yet. Current fallback: hello@execinspector.com