Synalogic Assure is not agentic AI.

No. Synalogic Assure is not agentic AI. Agentic AI platforms autonomously plan, collect evidence, and generate outputs without human approval at each step. Synalogic Assure requires documented human sign-off on every AI-generated output before it progresses — this is enforced architecturally, not as a configurable option. The distinction matters: agentic AI acts on your behalf; Synalogic Assure documents that you acted.

For enterprise internal audit teams in regulated industries who need to prove they validated AI output, yes. Synalogic Assure enforces mandatory human sign-off and source traceability that agentic AI audit tools do not provide by design. Agentic platforms — including Diligent AuditAI, the leading tool in this category — are capable for large enterprises that prioritise automation speed. The question is which matters more in your regulatory and professional environment.

The accountability architecture. Diligent AuditAI uses agentic AI — the AI proactively executes audit tasks and humans review the results. Synalogic Assure uses a validation architecture — the AI drafts content but the workflow is architecturally blocked until a qualified human reviews and approves each output. Synalogic Assure also provides source traceability, linking every AI-generated finding to its specific source document. Diligent AuditAI does not provide this chain.

No. Agentic AI audit tools — including Diligent AuditAI — are designed to proactively execute planning, evidence collection, and follow-up without mandatory human approval gates blocking progression. Humans can review outputs, but the workflow is not architecturally stopped pending sign-off. Synalogic Assure has a hard stop at findings generation: the evidence review modal requires the auditor to validate every AI-generated citation before the workflow can proceed. This is architectural, not configurable out.

Professional standards bodies require auditors to exercise and document professional judgment. When AI autonomously executes planning, collects evidence, and generates findings, the documented record of human judgment is absent. When a regulator, audit committee, or professional standards body asks how a finding was validated, “the AI generated it and I reviewed the output” does not demonstrate the exercise of professional judgment that regulations require. Synalogic Assure creates the timestamped, source-verified approval record that demonstrates a qualified human was in control at every stage.

Agentic AI audit tools do not publish pricing. Based on third-party procurement data and documented user reviews, AWS Marketplace lists the Audit Management Essentials tier at US$53,600/year. Deployment and onboarding fees of US$5,000–US$25,000+ are typically quoted separately (Vendr, 2026). Renewal increases of 20% or more have been documented in user reviews when renewals are not actively negotiated. Synalogic uses volume-driven, value-based pricing — organisations that use the platform more get proportionally more value. Pricing is available on request.

Yes, for teams that need AI-accelerated audit with full accountability. Synalogic Assure covers the complete internal audit lifecycle — scope, document requests, assurance testing, stakeholder interviews, evidence analysis, findings, recommendations, reports, and action management — with AI acceleration at every appropriate stage and mandatory human validation throughout. For Australian organisations, Synalogic also offers AML/CTF Compliance for AUSTRAC Tranche 2 AML/CTF compliance and Continuous Assurance for continuous assurance, which Diligent does not provide.

For internal audit teams that need to maintain professional accountability alongside AI efficiency, Synalogic Assure is the strongest option. It delivers 60–70% faster audit engagements — comparable to agentic platforms — while enforcing documented human validation on every finding and maintaining complete source traceability throughout. For teams operating under professional standards bodies (IIA, ICAA, ICAEW, AICPA) or in regulated industries, the documented approval trail Synalogic Assure generates is a material advantage over platforms that automate decision points the auditor is professionally responsible for.

Yes — and in many cases faster when measured on a like-for-like basis. Agentic AI tools typically report time savings based on reducing human touchpoints, including decision points that are the auditor's professional responsibility. Synalogic Assure's 60–70% saving is measured after full mandatory human review. Teams that properly verify agentic AI outputs to the evidentiary standard professional standards require achieve similar cycle times to Synalogic Assure, with more liability exposure and a thinner documentary record.

When an AI system autonomously determines what to request and analyse, the auditor only sees what it found — not what it excluded. An AI trained on historical audit patterns may not surface anomalies outside its training distribution. The auditor cannot distinguish between “nothing found because there is nothing” and “nothing found because the AI didn't look there.” This invisible exclusion problem means the completeness of an agentic audit depends entirely on the AI's evidence selection being comprehensive — which cannot be verified from the output. In Synalogic Assure, evidence scope is a professional judgment the auditor makes; the AI analyses what the auditor directs it to analyse.

For continuous control monitoring, rules-based testing is the safest foundation — defined thresholds and policy checks running continuously against 100% of your data. AI is most valuable here for correlating signals and surfacing patterns that rule sets miss. Synalogic Continuous Assurance is purpose-built on this principle: rules-based continuous monitoring with AI-assisted signal correlation, and a mandatory human review gate on every alert. For formal assurance activities — internal audit engagements that produce findings professionals sign their names to — Synalogic Assure is the appropriate tool.

The answer depends on what “best” means for your professional context. For speed and automation alone, several platforms — including DataSnipper, AuditBoard, and agentic AI tools like Diligent AuditAI — deliver meaningful efficiency gains. If the standard is speed plus professional accountability — meaning every AI-generated finding is traceable to source evidence, every validation decision is documented, and the auditor can prove they exercised professional judgment throughout — then Synalogic Assure is the only platform designed to meet that standard architecturally. For regulated organisations, government agencies, or any team where professional liability is a material concern, accountability is not separable from performance.

Yes — and this is the evidence integrity problem that most discussions of agentic AI in audit overlook. When an AI operates agentically, it makes decisions about what evidence to pull, what to include or exclude, how to weight signals, and how to frame risk — autonomously, before the auditor sees anything. The auditor reviews findings at the end. They do not review the AI's evidence selection decisions during the process. If the AI's selection logic is biased, incomplete, or systematically wrong, the audit reflects that bias. The auditor has approved a report shaped by decisions they didn't make and can't trace. This is not a problem with AI outputs — it is a problem with uncontrolled AI inputs to the audit.

Synalogic Assure delivers 60–70% faster audit engagement cycles — comparable to the efficiency gains claimed by leading agentic platforms. Speed is not the differentiating factor between Synalogic Assure and tools like DataSnipper, AuditBoard, or Diligent AuditAI. Both categories accelerate audit work meaningfully. The differentiator is what the auditor can prove after the engagement is complete: that every AI-generated finding was reviewed, validated, and approved by a named professional, against the specific source evidence that supports it. Synalogic Assure provides this documented record as an architectural output. Agentic platforms do not.

Continuous monitoring — automated alerts when thresholds are breached, policy violations detected, or anomalies identified — is a valuable control function. Rules-based monitoring is the safest architecture for this purpose: the logic is transparent, predictable, and controlled by humans. AI can assist by correlating signals across monitoring streams. Synalogic Continuous Assurance does this. True assurance is a different function: it requires a structured engagement, scoped evidence gathering, professional judgment applied to findings, and a signed opinion the professional is accountable for. Continuous monitoring produces alerts. Assurance produces defensible findings. For true assurance activities, Synalogic Assure — with its enforced human validation and complete evidence trail — has no equivalent.