Agentic process automation with an orchestrator, domain agents and a human who signs off
We build agent systems that take work from where it arrives, an email, a form, a sensor alert, through the systems it touches, and stop at the right person for approval. One orchestrator plans and tracks; domain agents for finance, HR, IT or procurement do the steps through governed connectors. Every action is logged and nothing irreversible happens without sign-off.
Why RPA bots broke and chat assistants never got the job done
Most enterprise work is coordination: read the request, check three systems, decide, update two of them, tell someone. Robotic process automation scripted that and broke whenever a screen changed. Chat assistants could explain the process but not run it. Between them sit the people who move the case along, re-keying between email, the ERP and a ticket queue.
This work is predictable in shape but variable in detail, the gap scripts cannot cover and language models alone cannot be trusted with. Someone still has to be accountable when a payment goes out.
Our approach to AI agents for business process automation
We separate planning from doing. An orchestrator receives the case, breaks it into steps and tracks state; domain agents scoped to finance, HR, IT or procurement carry out those steps using a fixed set of tools exposed through MCP-style connectors. Agents cannot improvise a new action. They call only tools they have been given, with permissions checked per call.
The system runs in three modes. Conversational, where a person asks and watches it happen. Monitoring, where agents watch mailboxes, queues or sensor feeds and act on what arrives. Scheduled, for month-end or every morning at six. Approval gates sit wherever money moves or records change, and an audit trail records what each agent saw, decided and did. Our AI Digital Employee packages this pattern for routine back-office roles.
What the solution includes
Six components. The connectors and approval gates are what make agents safe against production systems.
Workflow and exception discovery
We map the real process from tickets, emails and interviews, including the exceptions people handle quietly, and decide which steps agents own.
Orchestrator and domain agents
A LangGraph-style orchestrator plans and tracks each case. Domain agents with narrow scopes execute, so a procurement agent cannot touch payroll even if asked.
MCP-style connectors and tool adapters
Typed, permissioned tools over your ERP, HRMS, ITSM, email and document stores. Each tool declares inputs, outputs and the roles allowed to call it.
Approval checkpoints and policy enforcement
Gates where money, records or access change. The approver sees what the agent proposes and why, and approves, edits or rejects from email, chat or console.
State, retries and error handling
Checkpointed state per case, idempotent tool calls and typed failures, so a crashed run resumes instead of duplicating a payment.
Execution logging and audit trail
Every observation, decision, tool call and approval recorded with timestamps and identities, searchable by case for auditors and for improving the agents.
High-value use cases for agentic automation
The best candidates are multi-system processes with clear rules and a named approver. These six recur across finance, HR, IT and procurement.
Finance document workflows
Invoices, credit notes and expense claims read, matched, coded and queued for approval, then posted to the ERP, extending our document processing work with the approval and posting steps.
Employee onboarding and offboarding
An accepted offer triggers account creation, equipment requests and payroll setup across HRMS, ITSM and identity systems, with HR approving each sensitive step.
IT ticket triage and enrichment
Tickets classified, enriched with device and user context, routed to the right queue and, for defined cases such as access requests, resolved with approval.
Procurement and supplier administration
Purchase requests checked against budget and catalogue, supplier documents verified, purchase orders raised after approval and supplier records kept current.
Customer case triage
Cases from email and portals classified, matched to the customer record, enriched with order history and routed with a drafted response for a person to approve.
Monitoring-triggered operations
An infrastructure or facility alert starts a runbook: gather diagnostics, correlate with changes, open a ticket, propose a fix and wait for the on-call engineer.
How does an agentic workflow handle a case?
Six stages, with the human gate at stage five.
Trigger
A message, form, schedule or monitoring event opens a case and the orchestrator records its origin.
Plan
The orchestrator decomposes the case into steps, assigns each to a domain agent and notes which steps need approval.
Gather
Domain agents read what they need through connectors: the record, the policy, the budget line.
Propose
Agents produce the intended actions with reasoning and evidence, in a form a person can check quickly.
Approve
The named approver confirms, edits or rejects. Low-risk steps within policy can be pre-approved; everything else waits.
Execute and record
Approved actions execute idempotently, results are confirmed, and the trail is written to the audit log.
Deployment, connectors and integration
Agents run in your cloud tenancy or on-premises, with model calls routed to regional endpoints or models hosted in your environment. Connectors are MCP-style tool servers over your systems' APIs: Dynamics 365 and Business Central, SAP, Workday and other HRMS platforms, ServiceNow and Jira, Microsoft 365 and Google Workspace, with your identity provider for per-call authorization. Existing RPA bots can be wrapped as tools during migration. Our generative and agentic AI team builds and maintains the connectors.
Where we deliver: in Saudi Arabia, gates and audit logs are designed to NCA ECC-2:2024 and SDAIA PDPL, with SAMA governance for banks; in the UAE, UAE PDPL shapes which personal data agents may read and retain; in India, the DPDP Act 2023 and RBI or SEBI frameworks apply to financial workflows; in the United States, SOX controls over postings decide where approvals sit. Our offices support each region.
Benefits and success criteria
A baseline is taken from the current process before the first phase, on the same case types. The first phase reports the change.
Cases completed without rework
Share of cases closed by agents and approvers with no later correction.
Human time per case
Minutes of approver and operator time per case, and what they are now spent on.
Tool error and exception rate
Failed tool calls, policy blocks and escalations, with causes, so the connectors and rules improve each cycle.
Cycle time
Trigger to closure against the manual baseline, including time waiting for approval.
Implementation approach
A first phase covers one process, one domain agent and two or three connectors in one mode. Orchestrator, gates and audit trail are reused for every process after.
Map the process and its exceptions
Tickets, mailboxes and interviews reveal the real steps, the workarounds and the decisions that need a person.
Review data, permissions and policy
Which records agents may read, which actions need approval and which are forbidden, written as policy before any agent runs.
Build connectors and the first agent
MCP-style tools over the systems involved, then the domain agent and orchestrator graph, tested on replayed historical cases.
First phase with approvals on everything
Live cases flow through with every action gated. Approvers judge the proposals and verdicts are analyzed.
Relax gates where evidence allows
Steps with consistent approval and no corrections move to pre-approved within policy. The rest stay gated.
Add processes and modes
New domain agents, connectors and modes join the same orchestrator, with regression tests on every change.
We have built live LangGraph agent systems and we know where they fail: unbounded tools, missing state, approvals bolted on at the end.
Our design puts connectors, gates and audit trail first, which is why our agents can run against a live ERP. The same team builds the ERP side, including Business Central AL extensions, and the monitoring platforms that feed the monitoring mode. For voice or chat entry points the agents share their graph with our conversational AI solution, and for banks and insurers our BFSI practice brings the regulatory design.
Questions we are asked
How does an agentic automation engagement with Constient start?
With a process map. We take one process you want automated, read its tickets and mailboxes under NDA, interview the people who run it and return a map of steps, exceptions and decisions, marked by which an agent could own and which stay human. From that comes a scoped first phase with one domain agent, two or three connectors and the approval policy written out.
How is this different from RPA or a chatbot?
RPA scripts clicks and breaks when a screen changes; a chatbot answers questions but cannot act. Our agents plan a case, call typed tools through APIs rather than screens, and stop for approval where policy demands. Existing RPA bots can be wrapped as tools during migration, so you keep what works while brittle parts are replaced by connectors.
What stops an agent doing something it should not?
Three things. Agents can only call tools they have been given, and each tool checks the caller's permissions per call. Approval gates sit wherever money, records or access change, and nothing passes them without a named person. Every observation, decision and action is logged, so anything unexpected is visible and traceable to its cause.
Which systems can you connect to?
Anything with a supported API: Dynamics 365 and Business Central, SAP, HRMS platforms, ServiceNow, Jira, Microsoft 365, Google Workspace, document stores and your identity provider. We build connectors as MCP-style tool servers, so each is reusable across agents. Systems without APIs are reached through wrapped RPA or file interfaces as a transitional step.
Where do the agents and our data run?
In your cloud tenancy in the region you choose, or on-premises. Model calls can use regional endpoints or models hosted inside your environment, and agents read only the records policy allows. We document the design against SDAIA PDPL, UAE PDPL or the DPDP Act 2023, and sector rules such as SAMA or RBI frameworks, before any live case flows.
How long does a first phase take and how is it priced?
One process with one domain agent and two or three connectors typically reaches a live first phase with full approvals in 10 to 14 weeks. Mapping and first phase are a fixed fee. Scaling is priced per process and connector as a fixed scope, or as a dedicated agent engineering team, with model and hosting costs passed through.
Pick one process and we will map what an agent could own
Give us one case type that crosses email, your ERP and an approver. We will map it, show which steps agents can take safely, and scope a first phase with the approval policy written first.