Accordance’s public launch this week signals a new phase in AI for tax and accounting: the startup bills itself as an “AI brain” that bundles specialized regulatory knowledge, a multi‑agent reasoning system, and integrations designed for professional workflows — all backed by a recent $13 million funding round. At the same time, spreadsheet-first companies such as Sourcetable are pushing the same “agentic” envelope in the data layer, advertising Superagents that can reach any database, API or Model Context Protocol (MCP) server so that users can query and act on live business data from inside a sheet. Together these announcements illustrate two converging trends in accounting tech in 2025: domain‑specialized AI built for defensibility and auditability, and agentic connectivity that makes live systems directly available to LLM‑driven assistants. Both trends promise productivity gains — and raise important questions about accuracy, access control, and professional responsibility. (accountingtoday.com)
Legal exposure can arise when:
Source: Accounting Today Tech news: Accordance touts 'AI brain' for accountants
Background
What Accordance says it is building
Accordance has positioned itself as a frontier model company focused on tax, audit and accounting workflows. The company describes a multi‑agent system trained on an extensive library of federal, state and international codices — statutes, accounting standards, court precedents and treaties — and claims the result behaves as an “intelligent copilot” for accounting professionals. The launch announcement and press coverage also note strategic partnerships and research relationships with major AI firms, and a seed/pre‑seed funding package reportedly totaling $13 million led by Khosla Ventures and General Catalyst with participation from other investors. (accountingtoday.com)What Sourcetable is offering right now
Sourcetable, a spreadsheet‑centric data tool, has announced Superagents — an agentic AI connectivity layer that the company says can connect the spreadsheet UI to any database, API, MCP server or third‑party app on the internet. Sourcetable’s pitch is that users will be able to access and analyze data across business applications from inside a sheet without coding, and that the product’s AI chat can act as a connector wizard to guide users through linking external systems. That broader capability is positioned as the next step beyond natural‑language queries and automatic charting: it’s about giving agents live access to enterprise data and actions. (sourcetable.com)Why this matters to accountants and firms
Productivity and knowledge scaling
- Faster research and decision support. An AI system with curated tax and accounting knowledge can surface relevant statutes, citations and precedent much faster than manual research, shortening time to answer for complex client questions.
- Junior-to-senior leverage. By encoding professional heuristics and example reasoning, these systems can help less experienced staff produce higher‑quality drafts, freeing senior staff for judgment calls.
- Data-in-place analysis. Sourcetable’s approach brings heterogeneous operational data into conversation with models — enabling cross‑system analysis (ERP + CRM + bank feeds) without extraction and manual joins. (accountingtoday.com)
Workflow integration and defensibility
Accordance markets itself on deep domain alignment: training on authoritative corpora and producing cited outputs would be a major step toward defensible AI for regulated workflows. Firms increasingly demand tools that generate explainable outputs, citations, and an audit trail — not black‑box suggestions — because those characteristics map to professional standards and the documentation auditors or regulators expect. Sourcetable’s connector wizard also aims to reduce integration friction that historically slowed safe adoption. (accountingtoday.com)How these products fit into the agentic AI landscape
Agentic AI and the Model Context Protocol (MCP)
The recent conceptual shift to “agentic” AI — systems that plan, call tools, and act across steps rather than merely replying to prompts — has been crystallized by industry work on standards such as the Model Context Protocol (MCP). MCP (an open protocol championed in the ecosystem) standardizes how LLMs and agent hosts invoke external tools, read files, and exchange structured context across processes. That interoperability layer is precisely what makes claims like “connect to any MCP server” meaningful: if widely adopted, MCP allows discrete agents to talk to many tool providers in a consistent, auditable way. MCP’s rapid adoption in 2024–2025 has already influenced how platform vendors and enterprise software think about safe tool composition. (cloud.google.com)Incumbents are also moving agentic
Large professional content providers and software incumbents have been investing heavily in agentic, domain‑tuned assistants. Examples include Thomson Reuters’ rollout of agentic systems for tax and accounting and Wolters Kluwer’s GenAI enhancements for research platforms — both signal that the “trusted content + agentic tooling” pattern is now mainstream for professional services. That context matters: startups like Accordance face direct competition from incumbents that already own massive, licensed professional libraries and distribution channels. (thomsonreuters.com)Deep dive: Claims, verification, and open questions
Funding and traction: what’s verifiable
Accounting Today and multiple industry outlets reported Accordance’s public launch alongside a $13 million funding figure and named lead investors. Independent press coverage and venture trackers corroborate the funding details. These pieces of information are supported by both the company’s materials and third‑party reporting. (accountingtoday.com)Technical performance claims — treat with caution
Accordance’s press materials contain quantitative claims about model improvements (for example, percentage improvements on internal benchmarks such as “TaxBench”). Those claims come from the vendor and currently appear to be proprietary benchmarking results; independent reproduction or third‑party benchmark publication is not available in the public record at the time of reporting. In other words, the headline performance claims are plausible given domain‑specific fine‑tuning, but they should be treated as vendor‑reported metrics until validated by independent evaluators or in open benchmarks. Any purchase decision should require proof points under your firm’s own test cases. (accordance.ai)The connectivity promise — powerful but hazardous
Sourcetable’s Superagents promise frictionless connectivity: if it can truly reach “any” internet‑accessible database, API or MCP server, the practical upside is enormous for cross‑system analysis. But that same reach introduces risk vectors:- Credential exposure: Automatic connectors that require API keys or OAuth consent create new attack surfaces if not implemented with strict secrets handling and least‑privilege models.
- Prompt injection and tool chaining: Agentic flows that combine tools can accidentally escalate privileges or leak data across contexts if connectors and host clients do not enforce robust permission boundaries.
- Data provenance and audit trails: Real‑time access must be paired with immutable logs and explainable mappings from query → tool calls → outputs so professionals can justify conclusions to clients and regulators. (sourcetable.com)
Security, compliance and professional liability
Data handling: a checklist for firms evaluating these tools
- Encryption at rest and in transit: Confirm the vendor’s cryptographic posture and key management practices.
- Data residency and retention policies: Ensure document and client data storage locations comply with jurisdictional rules.
- No‑train guarantees: If the vendor claims customer data won’t be used to train shared models, require contractual commitments and technical isolation.
- Role‑based access and least privilege: Connectors should not grant blanket, highly privileged credentials to agent runtimes.
- Immutable audit trails: Every agent action, tool call and output should be logged in a way that maps back to user and client identifiers.
Professional standards and the “trust but verify” stance
AI outputs must not short‑circuit the professional judgment that accounting standards and ethical rules require. Tools that output tax positions, journal entry suggestions, or legal citations should be treated as drafts for human verification, not final opinions. Firms should update their internal quality‑control checklists and engagement letters to explicitly document human review expectations, error‑handling processes, and client consent for AI‑assisted work.Legal exposure can arise when:
- An AI provides incorrect tax advice that results in client penalties.
- A model hallucinates a non‑existent precedent or misquotes a statute.
- Data exfiltration via connectors compromises client confidentiality.
Practical adoption considerations for accounting firms
Start small, validate, and scale
- Pilot on low‑risk workflows. Begin with tasks like data cleaning, draft summaries, and templated disclosures rather than tax return computations or signed opinions.
- Design test corpora. Create a firm‑specific benchmark of representative tax and accounting problems and measure tool outputs for correctness, citations, and explainability before broader rollout.
- Embed human gates. Use multi‑step approval flows where the AI’s output is annotated and signed off by an identified professional before it reaches the client.
- Contractual clarity. Vendor contracts should explicitly address training, data use, breach response, and liability caps for erroneous outputs.
Training and change management
Adoption is as much about people as technology. Firms will need:- Training modules on “how to prompt safely,” interpret AI citations, and verify tool outputs.
- Updated engagement and disclosure language for clients that explains AI assistance and residual risks.
- Role definitions for AI reviewers and escalation paths for ambiguous results.
Competitive and market implications
Startups vs incumbents
Accordance’s domain focus and investor backing show confidence that startups can carve defensible niches in professional workflows by fusing proprietary knowledge corpora with agentic tooling. But incumbents — platforms that already control licensed professional content, client relationships and compliance features — are aggressively building agentic offerings too. That creates a bifurcated market:- Fast innovators offer point solutions and unique integrations that appeal to early adopters.
- Platform incumbents offer scale, integrated billing, and the ability to package agentic assistants with licensed content and SLA‑backed support.
The building blocks of an “agentic stack”
Software vendors and platform teams are assembling a recognizable stack:- Model hosts and task controllers (LLMs and multi‑agent orchestrators).
- Interconnect standards (MCP and tool‑calling interfaces).
- Connector layers (APIs, SCP/MCP servers that expose enterprise data).
- Governance and audit components (logging, certs, RBAC, explainability).
A healthy ecosystem will also include independent benchmarks and auditors specializing in LLM‑driven workflows. The breadth of that ecosystem will strongly influence who captures the most value: raw model suppliers, connector vendors, or firms that package end‑to‑end, compliant workflows. (cloud.google.com)
Technical and operational risks to monitor
Hallucinations and misattribution
Even domain‑adapted models can hallucinate or misattribute citations. For tax and accounting, incorrect citations or computed numbers are particularly dangerous because they may underpin client filings or advice. Firms must require verifiability at the output level — ideally machine‑checkable reconciliations and links to the originating authoritative source. (accordance.ai)Tool‑chain attacks and supply chain risk
Agentic systems orchestrate many tools. A malicious or compromised connector (or an incorrectly configured MCP server) can be used to exfiltrate sensitive files or to substitute malicious responses. Vendors and firms should assume adversaries will probe agentic flows and adopt defense‑in‑depth: MFA, ephemeral credentials, connector whitelists, and runtime permissions. (en.wikipedia.org)Model drift and governance
Models evolve. A system that performed well at pilot may behave differently after a model update or after the vendor fine‑tunes on new data. Firms must include model‑update clauses in vendor SLAs, require versioning for reproducibility, and maintain internal monitoring for drift or degradations in performance over time. (accordance.ai)Recommendations for buyers and technical teams
- Request a technical security pack: encryption details, penetration test results, SOC/ISO reports, and evidence of ephemeral secret handling for connectors.
- Require auditable citation and traceability: outputs must include the exact evidence sources and a machine‑readable lineage back to document or statute.
- Define a clear scope for AI use in engagement letters: clients must know when and how AI is used in their work.
- Run your own accuracy benchmarks: use representative client scenarios as acceptance tests before deploying for billable work.
- Segregate duties: separate account teams for AI‑assisted drafting and the human signatory who certifies work product — and log both decisions.
The bigger picture: what to watch next
- Standardization of connectors: MCP adoption is a key enabler. Watch vendor and OS‑level support (Microsoft and major cloud providers have already signalled MCP support) because interoperability will determine which tools gain traction. (theverge.com)
- Independent benchmarking in finance/tax: expect new public benchmarks (TaxEval variants, BizFinBench, TaxCalcBench) to appear; third‑party results will be critical for separating marketing claims from real capability. (vals.ai)
- Regulatory and standards responses: professional bodies and regulators will increasingly demand disclosure and accountability around AI‑assisted work; early alignment with standards (AICPA, local accounting boards) will be a competitive advantage. (sage.com)
Conclusion
The Accordance launch and Sourcetable’s Superagents are emblematic of 2025’s defining trend in professional software: agentic, domain‑aware AI that reaches across systems and furnishes professionals with faster, deeper answers. For accounting firms, those capabilities promise productivity and knowledge scaling but require a disciplined approach to security, verification and governance. The technology’s promise will be realized only when vendors can pair convenience with defensibility: provable, auditable citations; secure connector designs; and robust human‑in‑the‑loop controls. Firms that pilot aggressively yet govern conservatively will gain the most: they’ll harvest the benefits while minimizing the predictable hazards of agentic AI in professional practice. (accountingtoday.com)Source: Accounting Today Tech news: Accordance touts 'AI brain' for accountants