Governed Agentic Operations

AI agents that do your
operational work

Matching, investigation, and exception handling — governed, auditable, and embeddable via API.

workflow.json
{ "name": "invoice_reconciliation", "actions": [ { "type": "matcher", "matchOn": ["invoice_id"], "tolerance": 0.02 }, { "type": "loop", "mode": "react", "objective": "Investigate exceptions" }, { "type": "PbotApproval", "comment": "Review recommendation" } ] }
Core Architecture

Graduated exception handling

One pipeline. Three tiers. Each handles the cases it's best suited for — automatically.

10,000
records enter the pipeline
80% Deterministic Matching
15%
5%
Deterministic Matching
8,000 records
Exact keys, numeric tolerance, date windows, fuzzy text. Clear cases resolve instantly with zero ambiguity.
exact · tolerance · fuzzy
AI Investigation
1,500
ReAct agents analyze ambiguous exceptions with bounded reasoning and full traces.
bounded · traced
Human Review
500
Edge cases escalate with full context. Decisions enter the audit trail.
escalate · decide
100% AUDIT COVERAGE — EVERY TIER, EVERY DECISION
AI as Compiler

Describe it. Compile it. Run it.

AI generates the spec upfront. Execution is deterministic. Enterprises audit specs, not vibes.

01

Define in plain language

Describe the workflow. The AI compiler generates a deterministic JSON specification — the branches, conditions, escalation paths.

02

Execute deterministically

The compiled spec runs exactly as written. No improvisation. No hallucinated steps. Inspectable and versionable before any data is touched.

03

Reason within boundaries

When exceptions need investigation, bounded agents reason with declared tools and iteration caps. Every thought and action captured in the trace.

Generative AI

Prompt → Response → Done
Prompt Response

Agentic AI with Hyphen

Think → Act → Observe → Repeat until done
Think Act Observe bounded · traced · governed
The Gap

What production agents need

Getting agents from demo to production requires infrastructure that most teams don't have time to build.

Structural permissioning

Agents can only use tools explicitly declared in the workflow. They can't discover, invent, or call capabilities they haven't been given. This is architectural, not policy.

Reasoning traces

Every agent iteration captures what it thought, which tool it chose, the parameters it used, and the result it received. Complete chain of reasoning, queryable and auditable.

Bounded execution

Maximum iteration caps prevent runaway costs. Stuck detection identifies when agents loop without progress and triggers recovery — fail, retry with a hint, or escalate to a human.

Human-in-the-loop by design

Not a fallback. Not a safety net bolted on. Agents pause and present full context when confidence is low. The human's decision resumes execution and enters the audit trail.

Governed Orchestration

Three deployment patterns

The agent decides what to do. The spec defines what it's allowed to do.

Pattern A

Agent as Workflow Step

One reasoning step inside a deterministic pipeline. Match 10,000 invoices, hand 47 exceptions to an agent.

Step 1: matcher Step 2: react agent Step 3: approval
Pattern B

Agent as Trigger

Receives unstructured input, reasons about it, dispatches the right workflow. Intelligent routing without rules.

inbound doc classify + route invoice_wf tax_wf support_wf
Pattern C

Agent as Orchestrator

Coordinates multiple workflows. Decides sequence, synthesizes results, escalates when confidence drops.

orchestrator agent kyc_verify sanctions due_diligence synthesize → decide low confidence? → pause_for_human
Developer Experience

One API. Full pipeline.

Define workflows in JSON or generate from natural language. Register actions once, use everywhere.

  • Multi-criteria matching with tolerance, date windows, fuzzy text
  • ReAct agents with structural permissioning and reasoning traces
  • Human-in-the-loop as a first-class primitive
  • Multi-tenant isolation per organization by default
  • OAuth integrations for Gmail, Slack, Outlook
agent.json
{ "objective": "Investigate unmatched invoices", "tools": [ "lookup_erp", "check_payment_status", "gmail_send", "__pause_for_human__", "__complete__" ], "max_iterations": 10, "on_stuck": { "action": "escalate" } }
Differentiation

What nobody else ships

Built-in data matching

Multi-criteria matching engine as a first-class primitive. Exact keys, numeric tolerance, date windows, fuzzy text — structured exceptions that feed directly into investigation.

vs. every other platform → "build it yourself"

Governed agent orchestration

AI agents that reason within structural constraints. Tool permissioning at the architecture level, not as a policy layer. Bounded iteration with stuck detection and automatic recovery.

vs. LangGraph → no governance · vs. Temporal → no reasoning

AI as compiler, not runtime

AI generates the workflow spec upfront. Execution is deterministic. Auditors inspect the spec, trace every decision, reconstruct reasoning after the fact.

vs. runtime agents → powerful but unauditable at scale

Embeddable infrastructure

API-first. Multi-tenant by default. SaaS platforms embed Hyphen into their products. Your brand, your customers, our engine.

vs. agent platforms → they own the customer relationship
Use Cases

Where operations meet intelligence

Financial Reconciliation

Invoice-to-payment matching, bank reconciliation, intercompany settlement. Auto-reconcile, AI-investigate, human-review.

Insurance Claims

Claims-to-policy matching, duplicate detection, coverage verification. Graduated from auto-adjudicate to adjuster review.

Healthcare Revenue Cycle

Remittance-to-claim matching, denial management, underpayment detection. AI agents that analyze denial codes and recommend appeals.

Supply Chain

PO-to-invoice verification, goods receipt reconciliation, vendor compliance. Flag quantity and pricing discrepancies automatically.

KYC & Compliance

Identity verification, sanctions screening, adverse media. Auto-clear low-risk, AI-investigate medium, human-review high.

SaaS Embedding

Vertical SaaS platforms embed Hyphen to add governed agentic operations to their own products via API.

Get Started

Intelligence is cheap.
Liability is infinite.

Governed AI agents that reason and act on operational data. See results in days, not quarters.

Most teams go live with their first workflow in under two weeks. We help.