Agentic Process Automation
RPA couldn't think. Your AI agents can.
Agentic Process Automation services for back-office operations.
AI agents that handle exceptions, reason across systems, and run end-to-end back-office processes — invoice-to-pay, ticket triage, reconciliation, vendor onboarding. First production agent live in 30 days.
Workflow before → after
Invoice arrives (PDF, email)
Agent reads, codes, routes for approval
Support ticket lands in queue
Agent triages, resolves or escalates
Vendor onboarding (40-step checklist)
Agent runs KYC + document verification
30 days
First agent live
$999
Agent Sprint fixed fee
24/7
Autonomous ops
Agent Sprint — productized engagement
4 weeks. One production-ready agent.
The Agent Sprint is our productized entry engagement. We pick one back-office workflow with you, build and test the agent, deploy it with HITL guardrails, and hand over with eval harness + observability. No scope creep, no T&M.
Larger multi-agent programs run 8–12 weeks and are quoted per scope.
Agent Sprint
Timeline
4 weeks, fixed
Price
flat fee
Deliverable
One production agent, eval harness, observability dashboard, runbook
Best for
Testing the waters — or shipping one workflow fast
What comes next
Optional retained engagement for additional agents
The problem
You automated the happy path with RPA. Then every UI change broke your bots. Every exception got escalated to humans. Every new process required a full rebuild.
Meanwhile, your operations team still does this:
- Copies invoice data between SAP, your bank, and Excel
- Reads support tickets to figure out where to route them
- Reconciles payments across 5 systems
- Chases approvals across email, Slack, and SharePoint
- Onboards vendors with 40-step checklists
Agentic Process Automation (APA) replaces this work with AI agents that reason through exceptions — not just follow rules. They read unstructured data, call tools, handle edge cases, and escalate only when they should. Agents reason about the decision. The action is code.
Replace RPA with AI agents for business
APA implementation services bring intelligent process automation to the messy middle — invoices, tickets, approvals, onboarding — where hyperautomation services typically stall.
We help you keep RPA for deterministic steps and layer AI agents for back office decisions on top.
APA vs RPA — what's different
Most teams keep both: RPA for deterministic steps, APA for the judgment-heavy layer. We help you split the work correctly.
| Traditional RPA | Agentic Process Automation (APA) |
|---|---|
Core mechanism Rule-based scripts | Core mechanism LLM reasoning + deterministic tool calls |
Handles unstructured data No (brittle on OCR, free-text fields) | Handles unstructured data Yes (PDFs, emails, handwriting, varied formats) |
Exception handling Escalate to human | Exception handling Reason, act, or escalate with full context |
Maintenance overhead 40–60% of TCO is fixing broken bots | Maintenance overhead Lower — agents adapt to variations |
UI change resilience Breaks | UI change resilience Typically tolerates |
Best for High-volume, fully deterministic steps | Best for Messy middle — invoices, tickets, approvals, onboarding |
What we build (productized back-office automation)
This page focuses on productized, back-office process automation. For bespoke, unique-workflow agents (SDR research, custom analyst, domain-specific bots), see Custom AI Agent Development.
Finance & accounting agents
- Invoice-to-pay: capture → match → code → route for approval → post to ledger
- AR reconciliation across bank feeds, payment gateways, CRM
- Expense classification + policy checking
- Month-end close accelerators
IT & HR ops agents
- Tier-1 IT ticket triage, diagnosis, and resolution (password resets, access, provisioning)
- Employee onboarding / offboarding across 10+ SaaS tools
- Leave and attendance queries with HRMS write-back
- Policy Q&A with citations
Procurement & vendor agents
- KYC + vendor onboarding with document verification
- Procurement requisition handling
- Purchase-order matching + flagging
Support ops agents (shared with the Conversational AI product)
- Multi-step customer inquiries across order/billing/product systems
- Returns + refund processing
- Escalation routing with full context handoff
For bespoke builds outside these productized templates — new workflow, unique data model, proprietary logic — use Custom AI Agent Development.
How we build
Our stack (picked per use case): LangGraph, CrewAI, OpenAI Agents SDK, Claude Agent SDK, custom MCP servers. Telephony and messaging rails come from our own ConverseAI product — your LangGraph implementation partner when the workflow demands it.
Scope the workflow.
Map current process, exceptions, success metrics.
Design the agent.
Roles, tools, guardrails, handoff points, eval criteria.
Build and test.
Working agent in 2–3 weeks with a full eval harness.
Deploy with HITL.
Humans-in-the-loop for high-risk actions until confidence is proven.
Observe and tune.
Weekly eval reviews, guardrail updates, coverage expansion.
Every agent ships with
- Test suite + eval harness (not demo-ware)
- Observability dashboard (what it did, why, token spend)
- Guardrails (deterministic tool calls for irreversible actions)
- Clean handoff docs and runbooks
We don’t deploy what we can’t measure.
How it works
Week-by-week delivery for multi-agent workflow automation and intelligent process automation at scale.
Week 0 — Scoping call (free).
Pick a workflow, confirm scope, align on success metrics.
Weeks 1–4 — Agent Sprint.
Working agent with full test coverage. Demo weekly.
Weeks 5–6 — Deploy.
Shadow mode → HITL → autonomous. Monitored go-live.
Month 2+ — Scale.
Expand coverage, add workflows, retainer-based ops support.
Why ConverseAI
- Eval-first delivery. Every agent ships with a test suite. We don’t deploy what we can’t measure.
- Depth over breadth. We pick the right stack per use case (LangGraph, CrewAI, n8n, native APIs) — no lock-in.
- Integration native. Our Conversational AI product already integrates with 50+ tools. Those connectors carry over.
- India + US delivery. 30–50% below US boutiques, same engineering quality.
- Product discipline. We run agents at scale in our own SaaS — not a services shop learning on client bills.
Outcomes you can expect
- Cut invoice-to-pay cycle from 9 days to 18 hours
- Automate 70–80% of Tier-1 IT/HR tickets end-to-end
- 1 agent replaces 3–5 FTE-hours per day on reconciliation
- First production agent live in 30 days via Agent Sprint
- 24/7 autonomous ops across 20+ SaaS tools with one orchestrator
How we compare
Agent Sprint vs RPA platforms vs custom dev shops.
| ConverseAI Agent Sprint | UiPath / Automation Anywhere | Custom dev shop | |
|---|---|---|---|
| Timeline to first agent | 4 weeks fixed (Agent Sprint) | 8–16 weeks | Unpredictable |
| Core mechanism | LLM reasoning + deterministic tool calls | Rule-based + limited AI layer | Custom code, no standard pattern |
| Exception handling | Agent reasons, acts, or escalates with context | Escalate to human | Manual scripting |
| Maintenance overhead | Low — agents adapt to variations | 40–60% of TCO fixing broken bots | High — no eval harness |
| Deliverable | Agent + eval harness + observability + runbook | Configured flows + support contract | Code only |
| Best for | Messy middle — invoices, tickets, approvals, onboarding | High-volume, fully deterministic steps | One-off prototypes |
FAQs
What is agentic process automation?
APA uses LLM-powered agents that reason, read unstructured inputs, and make decisions — going beyond rule-based RPA. Agents handle the exceptions RPA used to escalate.
How is APA different from RPA?
RPA automates the happy path with deterministic rules. APA adds reasoning — so agents handle variations, unstructured data, and exceptions. Most companies run both: RPA for high-volume deterministic steps, APA for the messy middle.
Will agents hallucinate on financial data?
Not when designed correctly. Deterministic tool calls for irreversible actions, HITL approvals on high-risk steps, strict guardrails. Agents reason about the decision; the action is code.
How do we measure ROI?
Cycle time, cost-per-transaction, FTE hours reclaimed, exception rate, error rate. Metrics defined with you in week 1 and tracked weekly post-launch.
Does our data train your models?
No. We deploy in your cloud (AWS/Azure/GCP) or via API providers with zero-retention settings. Your data stays yours.
What if the agent breaks?
Observability, eval harnesses, fallback workflows, and a monitored alerting channel. Breaks are visible in minutes, not discovered by customers.
Which processes can AI agents automate?
Back-office processes with structured or semi-structured inputs: invoices, tickets, reconciliation, onboarding, policy Q&A, ticket triage, approvals. If it involves reading unstructured data, making a decision, and updating systems, it’s agent-shaped.
Build vs buy — why not just use UiPath Agents or Automation Anywhere?
Platform agents work for standard patterns. For deep custom logic, integration depth, or cost-sensitive mid-market, custom builds are 30–60% cheaper long-term and more flexible. We help you pick honestly.
Timeline to first production agent?
4 weeks via Agent Sprint. Larger programs run 8–12 weeks for the first production cohort.