Enterprise AI Operations · Workflow Intelligence and Governance
Which workflows can you safely automate with AI?
And how will you govern them in production?
Two questions every enterprise AI leader now has to answer — most without the data to answer either one. TNDRL observes how work actually runs across desktops and browsers, scores every workflow for automation readiness, and governs every agent action at runtime against guardrails learned from real execution.
Two questions.
One platform.
First in pilots. Then in production. Then at the board. Every enterprise rolling out AI lands here — and TNDRL answers both from the same observed evidence.
Which workflows can we safely automate with AI?
Most teams are guessing — relying on process docs, SME interviews, or vendor claims. None of those see the exceptions, handoffs, and judgment calls where automation actually breaks.
Fiber observes real execution across desktops and browsers. Weave scores each workflow for Automation Readiness across seven weighted dimensions — and withholds the score when observation is insufficient.
How do we govern those workflows once they are automated?
Deploying an agent is the easy part. Keeping it in line once it's running is harder — allowed paths, escalation rules, drift detection, and an audit trail for every decision.
Sprout generates a Living Blueprint with guardrails learned from observation already attached. Trellis evaluates every agent action at runtime — allow, escalate, or block, logged and auditable.
The Living Blueprint answers both questions at once — what's automatable, and the guardrails that govern it in production.
Market reality
Every platform is shipping AI agents.
None of them score the risk first.
Reads system logs. Misses the exceptions, escalations, and workarounds that happen between systems.
Blind to where automation breaksScreenshots capture pixels, not meaning. Can't distinguish a routine step from a critical judgment call.
No context, no governanceMonths of interviews. Captures the official process, not the real one.
Outdated before it's doneThe happy path is easy to automate.
The rest is where it breaks.
of AI pilots deliver no measurable P&L impact. The missing ingredient: understanding how work actually runs.
MIT, The GenAI Divide, 2025of agentic AI projects will be canceled by 2027 — due to unclear value, escalating costs, or inadequate risk controls.
Gartner, 2025of enterprises have scaled AI agents to deliver tangible value. The rest are stuck in pilot limbo.
McKinsey, 202651% of firms report AI incidents. Only one-third of organizations with AI in core operations have mature governance controls in place. McKinsey, State of AI Trust, 2026
TNDRL sees what they miss — and answers both questions from the same observed evidence.
What TNDRL delivers
A Workflow Twin. A score. A Living Blueprint. Governed runtime.
A Workflow Twin of every process, built from observed execution — not documentation, not interviews, not system logs. An Automation Readiness Score across seven dimensions that tells you what's safe to delegate, withheld when observation is insufficient. A Living Blueprint — the deployable automation, with guardrails learned from observation already attached. Governed runtime — every agent action evaluated against the Blueprint's boundaries before it executes.
Observe. Score. Build. Govern. One continuous loop.
How it works
From observed work to governed runtime.
In weeks, not months.
Collectors deploy in days. First Automation Readiness scores land in weeks — not after a months-long process-mining engagement. Living Blueprints ship only when observation is sufficient. Trellis keeps evaluating every agent action once they do.
See the exceptions nobody documented
Lightweight collectors observe workflow execution across desktop apps, browsers, and cross-system handoffs. Within two weeks, TNDRL surfaces the real workflows — including the exception paths, escalation logic, workarounds, and shadow processes that no documentation captures. Metadata only. No screenshots. No PII.
Score every workflow for Automation Readiness
TNDRL assembles a living model of each process and scores it across 7 dimensions — backed by observed execution data, not documentation. You see which workflows are safe now, which carry exception patterns that need controls first, and where the biggest risk lives.
Generate a Living Blueprint with guardrails built in.
Sprout turns observed sessions into the deployable automation via causal variant analysis — approved paths, blocked paths, escalation rules. The boundaries learned from observation travel with the blueprint, not tacked on after.
Evaluate every agent action at runtime.
Trellis evaluates every action against the Living Blueprint's guardrails — allow, escalate, or block, logged and auditable. Drift is detected as real work diverges from the model and fed back to Fiber, so the loop stays closed.
Platform
Four modules.
One continuous lifecycle.
Fiber sees the work. Weave scores it. Sprout generates the Living Blueprint. Trellis governs every action at runtime. Drift detected during governance feeds back to Fiber — the loop stays closed.
Fiber sees. Weave scores. Sprout builds. Trellis governs.
Sprout output · What a Living Blueprint is
Observed workflow in. Living Blueprint out.
The Blueprint isn't a recommendation or a diagram. It's an executable governance artifact — the guardrails an agent runs against at every action.
- Runs observed142
- Happy path coverage75%
- Variants mapped9
- Exception paths4
- Systems crossed6
- Escalation triggers3
generates
Differentiation
Why existing approaches aren't enough
| TNDRL | Process Mining | Task Mining | Agent Orchestration | |
|---|---|---|---|---|
| What it sees | Behavioral execution — decisions, exceptions, escalations, cross-app sequences | System event logs — what software recorded, not what people did | Screen pixels — actions without operational meaning or context | Agent tool calls only — no visibility into how the work actually runs |
| Exception handling | Maps every exception path, escalation, and workaround automatically | Only sees exceptions that generate system events | Captures screenshots of exception screens without understanding why | Retries and re-prompts at the agent layer — no operational model |
| Automation safety | Dimensional readiness scoring across 6 risk factors, plus evidence-sufficiency gate — we withhold a score when observation is insufficient | No pre-automation safety scoring | No pre-automation safety scoring | Routing and policy checks — not evaluated against observed behavioral reality |
| Runtime governance | Living Blueprints with approved paths, blocked paths, and escalation rules — learned from observation | Post-hoc conformance checking against modeled process | No runtime governance capability | Static guardrails and tool allowlists — not grounded in workflow evidence |
| Drift detection | Continuous behavioral monitoring. Drift feeds back into observation — the loop closes. | Detects drift in system logs only — misses behavioral drift | No continuous monitoring | No drift signal against the human workflow — agent-side only |
| Privacy model | Metadata-first. No screenshots. No PII captured. | No screen data. Relies on system logs (may contain PII in event payloads). | Screenshots capture everything on screen — PII, PHI, credentials | Depends on upstream tool access — not a collection layer |
Harness-agnostic by design
Pick any agent harness. TNDRL decides which workflows are safe for a harness to run — and evaluates every action it takes against guardrails learned from observation.
Anthropic Managed Agents · OpenAI Agents SDK · Google Vertex Agent Engine · Microsoft Foundry Agent Service
The harness decides how. TNDRL decides whether.
Patent pending — provisional filed April 14, 2026 covering six families across the closed loop: behavioral observation, dimensional scoring, Living Blueprint generation, and runtime governance.
The math behind the score
Here's what's in the composite.
Two co-equal halves. Seven weighted dimensions. Interaction penalties where dimensions conflict. An evidence-sufficiency gate that withholds the score when observation is insufficient — because a confident-looking number you can't trust is worse than no number at all.
Automation Readiness
Execution Risk 71 (Complexity 76, Data structure 89, Exception rate 48, Compliance risk 84)
Two penalties triggered · net composite 74.
Below 50: redesign first. 50–70: redesign or tight guardrails. 70–85: safe with guardrails. Above 85: safe to delegate.
Score withheld if observation insufficient
Process Stability
Can an agent run this workflow predictably?
Execution Risk
If automation misbehaves, how bad is the damage?
Interaction penalties pair a Process Stability dimension with an Execution Risk dimension, catching the looks stable but execution is risky patterns a linear combination would miss. Each penalty is a filed claim, not a heuristic.
Diagnostic Lenses
Shannon entropy of the variant frequency distribution. Explains why a workflow's AR is what it is — high entropy means agents face unpredictable branching.
Ratio of value-adding work to total elapsed time. Surfaces where the time is going — wait states, handoffs, rework — and often drives redesign-vs-automate decisions.
Honest scoring · Evidence-sufficiency gate
When we haven't seen enough executions to score a workflow responsibly, we tell you we don't know.
Every Automation Readiness Score carries an admissibility check. If the observational basis is insufficient — too few runs, too much variance, not enough exception coverage — the score is withheld and the reason is surfaced. We would rather defer a judgment than fabricate one.
System architecture
How the pieces fit together.
Five deployment artifacts, two trust boundaries, one closed loop — metadata crosses, PII doesn't.
Where TNDRL runs
Built for structured operational work.
TNDRL is defined by operating model, not vertical. Wherever teams run multi-step, multi-system, exception-heavy work, the behavioral execution layer applies.
If the work is structured, multi-step, and exception-heavy, TNDRL applies. Ask how.
Security & Privacy
Privacy by architecture
TNDRL captures the structure of work, not the underlying data. Process metadata, decision points, and timing patterns are analyzed. PII, financial data, and health records never leave your perimeter.
Metadata only
Application names, field names, timing, workflow variants. No PII. No screenshots. Field values never leave your environment by default.
Sanitized proxy
Values stripped, patterns preserved. Used only for validation in air-gapped environments.
Raw data
Stays on-premises only. Never transmitted. Available for high-security local processing.
FIELD NAMES, NEVER FIELD VALUES
Trust posture · Precise, not aspirational
- ·Metadata-only architecture with source-side masking
- ·Field-name-only capture — never field values
- ·Configurable exclusion lists and collection policies
- ·On-premises deployment option for regulated data
- ·Security review packet with architecture and controls map
- ·Full audit trail of what was collected and what was not
Responsibilities mapped. Architecture and controls designed to satisfy the requirements buyers ask about most:
- ·SOC 2 Type II
- ·HIPAA BAA
- ·PCI DSS
- ·Regional data residency
- ·Customer-managed keys (CMK)
The 30-Day Evaluation
A concrete evaluation path. Not a multi-month implementation.
Lightweight collectors deploy on a target team's desktops and observe real work silently. You see a Workflow Twin in two weeks and a full Automation Readiness map in thirty days — on workflows that matter to your business, not a sandbox.
Collectors installed on a target team. Collection policy and exclusions configured centrally. No workflow changes required.
Continuous behavioral observation assembles the first living Workflow Twin — variants, exceptions, timing, and decision logic mapped from reality.
Automation Readiness scored across seven dimensions with interaction penalties and the evidence-sufficiency gate. Safe-to-delegate vs. needs-human-judgment, per workflow.
Walkthrough of the readiness map, the top candidates, the workflows to leave alone, and the guardrails the Living Blueprint would carry if promoted.
A Workflow Twin per observed process, an Automation Readiness map, and a written recommendation on where automation is safe, where it is not, and why.
One target team, IT sign-off on collector deployment, and a short scoping call. No data migration, no workflow re-engineering, no change management.
Source-side masking, field-name-only capture by default, configurable exclusions, and a full audit trail of what was collected and what was not.
Get Started
See your Automation Readiness Score before you automate.
See TNDRL build a living process model from real behavioral data, surface the risk your agents would miss, and score every workflow before automation begins. No slides. Working product.
Built by operators who've led enterprise automation programs.
FAQ
Common questions
TNDRL is the workflow intelligence and control platform for enterprise AI — built on the behavioral execution layer. Four modules running as one closed loop: Fiber observes how work actually runs — including exceptions, escalations, workarounds, and judgment calls that happen between systems. Weave builds a living Workflow Twin of each process and scores it for Automation Readiness — with an evidence-sufficiency gate that withholds a score when observation is insufficient. Sprout generates a Living Blueprint — the deployable automation, with guardrails learned from observation already attached. Trellis governs every agent action at runtime against those guardrails. Patent pending.
A Workflow Twin is a continuously updated digital representation of how a process actually runs — built from real behavioral observation, not documentation or interviews. It captures every step, variant, exception, decision point, and timing pattern. Unlike static process maps, a Workflow Twin evolves as your operations change, so it always reflects current reality. Deep dive →
AI agents execute tasks, but they operate on a narrow view of the process — the documented happy path. TNDRL provides the missing context: how the work actually runs, where it breaks, and what the risk profile looks like before anything is automated. Before agents act, TNDRL scores the risk. After they act, TNDRL monitors for drift and enforces compliance.
No. TNDRL is the governance layer around your harness. An agent harness — Anthropic Managed Agents, OpenAI Agents SDK, Google Vertex Agent Engine, Microsoft Foundry Agent Service — provides the runtime infrastructure for an AI agent: model invocation, tool orchestration, session state, sandboxed execution. TNDRL does none of that. TNDRL tells you which workflows are safe for a harness to run before it runs them, and evaluates every action the harness takes against guardrails learned from observation. Harness-agnostic by design. The harness decides how; TNDRL decides whether.
Process mining reconstructs workflows from system event logs — necessary context, but not sufficient. It misses the work that happens between systems: decisions, workarounds, shadow processes, and cross-application sequences. Some process mining vendors now claim event logs are an adequate context layer for AI agents. TNDRL provides the behavioral context layer — the missing dimension that makes agent context complete.
A TNDRL automation blueprint is a machine-readable, governed spec built from real behavioral observation. It includes every step, variant, exception, and decision point — plus allowed paths, blocked paths, safety thresholds, and rollback triggers. It evolves as operations change, so your automation stays current.
No. TNDRL uses a metadata-first architecture. It captures application names, field names, timing, and workflow patterns — never the underlying business data. PII, financial records, and health data never leave your premises.
Every automation blueprint includes governance rules: allowed variants, blocked paths, safety thresholds, and rollback triggers. TNDRL enforces these at runtime, continuously monitoring execution and flagging when reality drifts from what was approved. Think of it as a compliance layer that travels with your automation.
Weave evaluates workflows across weighted behavioral dimensions organized into two co-equal sub-components — Process Stability (Consistency, UI stability, Repetition) and Execution Risk (Complexity, Data structure, Exception rate, with Compliance risk shipping next). Interaction penalty functions pair a Process Stability dimension with an Execution Risk dimension, catching compounding risk a linear combination would miss. Rather than a binary yes/no, the Automation Readiness Score tells you which workflows are safe now, which need guardrails, which need redesign first, and which should stay human. You see the full risk picture — exactly what you're committing to before you commit. Deep dive →
Automation Readiness is THE score — the composite that answers whether a workflow is safe to delegate to an agent, computed across two co-equal sub-components: Process Stability (Consistency, UI stability, Repetition) and Execution Risk (Complexity, Data structure, Exception rate, with Compliance risk shipping next). Entropy and Flow Efficiency are diagnostic companions — not peer scores. Entropy quantifies behavioral variability observed across the session population; Flow Efficiency measures the ratio of value-adding work to total elapsed time. Both explain why an AR score is what it is, but neither is a decision signal on its own.
TNDRL deploys lightweight collectors on a target team's desktops. Within two weeks, you see your first Workflow Twin and Automation Readiness Scores for real workflows. In 30 days, you have a readiness map of your top automation candidates. No multi-month implementation. No change management required for the pilot. The collectors run silently alongside normal work. See the full evaluation process →