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AI Workflow Agent
Most processes are already automated in the middle. What still consumes a team is the start, the exceptions and the chasing. A workflow agent takes those.
RPA got you to eighty percent
The remaining twenty percent is where the headcount still is.
Robotic process automation handles structured, repetitive steps well. It fails on unstructured intake — an email describing a request in prose, an attachment in an unfamiliar format, a request that spans two processes at once — and every exception becomes a manual queue.
A workflow agent sits at exactly that boundary. It reads the unstructured intake, decides what the request actually is, validates it against your rules and master data, executes across systems, and escalates with a clear explanation when it cannot proceed. Where you already have RPA that works, we call it rather than replace it.
- Unstructured intake handled — email, PDF, prose, mixed requests
- Requests spanning multiple processes correctly split
- Validation against master data before any write
- Exceptions explained in business terms, not stack traces
- Approvals routed to the named authority with context
- Existing RPA bots invoked as tools rather than replaced
Processes commonly handed to a workflow agent
Order intake
Orders arriving by email and PDF, validated against pricing and stock, entered into the ERP.
Invoice processing
Extraction, three-way matching, exception explanation and approval routing.
Employee onboarding
Account creation, equipment requests, policy acknowledgements and manager checkpoints across systems.
Change requests
Classify, check impact, route for approval and update the record of change.
Claims intake
First-notice capture, completeness check, document chasing and triage to the right handler.
Reconciliation exceptions
Investigate mismatches across systems and propose or apply the correction.
Systems it operates across
- ERP: SAP, Oracle, Dynamics 365, Odoo, custom
- Finance and accounting platforms
- Ticketing and workflow tools
- Email and shared mailboxes as an intake channel
- Document stores and file-drop locations
- Existing RPA bots invoked as tools
- Databases and internal APIs
Ten stages from first call to a system your team trusts
Every AI engagement runs this sequence. Small projects compress stages; regulated projects expand them. Nothing gets skipped silently.
Discovery
A working session with your operations and engineering leads to map the process, the systems it touches, and where the cost actually sits.
AI Opportunity Assessment
We score candidate use cases on data readiness, volume, error tolerance and payback, then rank them. Some come back "do not use AI for this" — you get that answer too.
Solution Architecture
Model selection, retrieval design, tool boundaries, data flow, failure modes and hosting topology, documented before code.
Proof of Concept
A narrow build against your real data to prove accuracy on the cases that matter, typically 2–4 weeks. Go / no-go decision at the end.
MVP
One workflow, end to end, in the hands of real users. Evaluation sets and quality thresholds are defined here, not retrofitted.
Production Development
Hardening: error handling, retries, fallbacks, cost controls, rate limits, observability, and a human escalation path for every automated decision.
Integration
Wiring into your CRM, ERP, HRMS, data warehouse, ticketing and messaging channels through APIs, webhooks and event queues.
Security Testing
Prompt-injection testing, access-control verification, PII handling review, dependency scanning and penetration testing before go-live.
Deployment
Staged rollout on your cloud or ours, with CI/CD, versioned prompts and models, and rollback in place from day one.
Monitoring & Optimization
Quality dashboards, drift detection, cost-per-transaction tracking and a retraining or re-prompting cadence agreed in writing.
Security-conscious architecture, from the first design review
Enterprise AI fails on governance more often than on models. Every system we build is designed to support enterprise security requirements and to give your risk team answers rather than assurances.
Data privacy & residency
Your data stays in the region and tenancy you nominate. We architect for no-training-on-your-data configurations and document exactly which vendor endpoints see which fields.
Role-based access control
Retrieval and tool permissions inherit your existing roles. A user cannot surface a document through the AI that they could not open directly.
Authentication & authorization
SSO via OIDC/SAML, short-lived tokens for agent tool calls, and per-tool scopes so an agent holds the narrowest possible privilege.
Encryption
TLS in transit, AES-256 at rest, managed keys via your cloud KMS, and encrypted vector stores for embedded content.
API security
Gateway-level authentication, signed webhooks, IP allowlisting, request validation and quota enforcement on every exposed endpoint.
Audit logging
Every prompt, retrieval, tool call, model version and human override is logged with a trace ID, so any output can be reconstructed months later.
Data isolation
Per-tenant separation at the storage, index and key level for multi-entity groups and regulated environments.
Secure prompt handling
System instructions are server-side, user content is treated as untrusted input, and we test against prompt-injection and tool-abuse patterns.
PII protection
Detection, masking or tokenisation of personal data before it reaches a model, with configurable redaction policies per field.
Human approval workflows
High-impact actions — payments, refunds, contract sends, record deletion — route to a named approver instead of executing autonomously.
Monitoring & anomaly detection
Alerting on unusual tool usage, cost spikes, refusal rates and quality regressions.
Rate limiting & abuse control
Per-user and per-tenant throttles, spend caps and circuit breakers so a runaway loop cannot become a runaway invoice.
Secure deployment
Private networking, secrets in a managed vault, immutable builds, dependency scanning, and infrastructure as code.
Questions enterprise buyers ask us first
How is this different from RPA?
What happens with a request it has never seen?
Can it write directly into our ERP?
How long does a workflow agent take to build?
Related AI services
Talk to an AI solution architect
No junior sales rep, no discovery deck. The person on the call is the person who will design the system.
- Response within one business day
- Mutual NDA signed before detailed discussion
- Written scope, one price, one delivery date
- You own all source code, models and IP at launch
Bring us the process that is costing you the most.
Book a 45-minute call with a solution architect. You leave with a use-case shortlist, a reference architecture sketch and a realistic build envelope — whether or not you build it with Ezulix.