When it comes to food, few combinations are as timeless as rice and peas. Growing up in the Caribbean, this dish was a staple at the heart of many meals I enjoyed. For me, nothing beats a plate of rice and peas served with stew goat and rich, flavorful sauce.
What I didn’t realize at the time was that this simple, balanced dish would later shape how we think about building intelligent systems at TactML.
Just like preparing a great meal, developing effective agentic accounting solutions requires the right ingredients—carefully selected, thoughtfully combined, and consistently refined.
At TactML, we focus on five core ingredients as we build ABLE, our AI-powered accounting copilot:
- RICE – Reasoning Intelligence Customised for Enterprises
- PEAs – Precise Enterprise AIs
- GOAT – Governance Alignment Testing
- MEAT – Model Ethics Alignment Testing
- SAUCE – Secure, Auditable, and Continuously Evaluated
In the sections that follow, we break down each of these components and explore how they come together to deliver safe, reliable, and high-impact agentic accounting.
Why Agentic Accounting Needs a Recipe
Agentic accounting moves beyond rule-based automation. Instead of simply following predefined workflows, AI agents can interpret context, make decisions, and take action.
But most firms are still approaching AI as incremental automation—faster data entry, better classification, marginal efficiency gains. That’s a mistake.
Agentic accounting isn’t just about doing the same work faster. It’s about building systems that can reason, act, and operate within the same constraints as a trained accountant.
Without the right structure, these systems can:
- Misclassify transactions
- Apply incorrect accounting treatments
- Produce outputs that are difficult to audit or defend
That’s why a recipe-based approach matters—especially in a domain where trust, accuracy, and accountability are non-negotiable.
RICE: Reasoning Intelligence Customised for Enterprises
RICE is the reasoning layer that allows AI to “think” like an accountant and execute complex workflows. In practice, this shows up in scenarios like:
Handling edge-case transactions
For uncertain bank transactions with low confidence scores, an agent can investigate, suggest invoice matches or expense categories, and explain its reasoning to the user.
Revenue recognition decisions
An agent evaluates whether revenue should be recognized immediately or deferred based on contract terms—not just rules. It can then prepare the appropriate journal entry and route it for approval.
Crucially, this reasoning is customised to the firm:
- Local accounting standards (e.g. IFRS vs GAAP)
- Firm-specific policies (e.g. capitalisation thresholds)
- Client-specific nuances
Without this layer, even advanced AI behaves like a generic assistant—not a trusted accounting copilot. The outcome is reduced manual exception handling, more consistent decision-making, and higher confidence in complex accounting treatments.
PEAs: Precise Enterprise AIs
While RICE provides general reasoning, PEAs are specialized AI models designed for high-volume, repetitive tasks where precision is critical.
In an accounting firm, this could include:
AR/AP classification models
Extract data from vendor bills or sales invoices, validate it, and post it to the correct accounts
Bank transaction classification and matching
Interpret transaction context to classify bank transactions and match them to open invoices or bills.
The key is narrow scope + high accuracy.
Instead of relying on a single system to do everything, PEAs:
- Focus on specific tasks
- Reduce error rates
- Deliver predictable, testable outputs
This modular approach also allows firms to adopt AI incrementally, without overhauling entire processes.
The outcome is faster processing of routine tasks, reduced manual workload, and improved accuracy across high-volume workflows.
GOAT: Governance Alignment Testing
In accounting, capability without control is a liability.
GOAT ensures that every agent operates within governance frameworks, including regulatory requirements, internal controls, and risk policies.
Real-world examples include:
Approval thresholds
An agent can propose journal entries, but anything above a defined materiality level requires human approval. Similarly, low-confidence classifications are automatically routed for review.
Regulatory compliance checks
Ensuring VAT treatments, tax codes, and reporting standards are correctly applied before entries are finalized.
GOAT acts as a built-in control layer, ensuring that:
- Agents operate within defined boundaries
- Decisions remain compliant
- Firms maintain audit readiness
The outcome is reduced compliance risk, stronger internal controls, and greater confidence in AI-assisted workflows.
MEAT: Model Ethics Alignment Testing
Accounting isn’t just technical—it involves judgment and professional responsibility.
MEAT ensures that AI systems behave responsibly when making or supporting those decisions.
Practical examples include:
Bias in anomaly detection
Preventing systems from disproportionately flagging certain vendors or clients due to skewed historical data.
Transparency in recommendations
When suggesting a write-off or adjustment, the agent explains why, not just what.
Consistency across clients
Ensuring decisions are applied fairly, rather than optimized purely for efficiency or profitability.
This layer reinforces:
- Professional integrity
- Client trust
- Internal accountability
The outcome is more transparent, explainable AI systems that align with both professional standards and firm values.
SAUCE: Secure, Auditable, and Continuously Evaluated
No great meal is complete without a flavorful sauce. In this framework, SAUCE is what makes the system production-ready.
In an accounting firm, this translates into:
Audit trails
Every AI-generated recommendation or action is logged with, inputs used, reasoning steps and final outputs.
Continuous evaluation
Tracking performance over time, such as, acceptance vs. rejection rates of AI suggestions, classification accuracy, precision and recall scores.
Security controls
Ensuring sensitive financial data is access-controlled, encrypted and never exposed unnecessarily.
SAUCE ensures that firms don’t just use AI—they can trust, monitor, and defend it.
The outcome is full auditability, continuous improvement, and enterprise-grade security.
Bringing It All Together
Consider a typical month-end close for a mid-sized client.
With the full framework in place:
- RICE interprets transactions and determines appropriate accounting treatments
- PEAs handle AR/AP postings, bank classification, and reconciliations
- GOAT enforces approval workflows and compliance checks
- MEAT ensures decisions are fair, transparent, and explainable
- SAUCE logs every action for audit and continuous improvement
Instead of chasing spreadsheets, reconciling discrepancies manually, and coordinating across teams, the firm operates with a coordinated system that:
- Surfaces issues proactively
- Suggests and prepares adjustments
- Routes decisions to the right people
The result is a faster, more reliable close process—with fewer surprises and greater confidence in the numbers.
Conclusion: From Kitchen to Capability
Just like a great dish, building agentic accounting systems isn’t about any single ingredient—it’s about balance.
At TactML, ABLE is being designed with this philosophy at its core. By combining reasoning, precision, governance, ethics, and continuous evaluation, we’re creating systems that accounting firms can trust—and that scale with them.
Because in both cooking and AI, the difference between something that works and something that truly excels comes down to how well the ingredients come together.
