Process Automation Agents
A multi-step agent orchestration layer — workflows expressed as agent-coordinated processes, integrated with the business's existing systems, with human-in-loop checkpoints where the workflow needs them.
- Engagement
- 8–14 week build · ongoing operation
- Built for
- COOs · Operations directors · CIOs
Cross-functional processes — onboarding a new customer that touches sales, finance, support, and product; processing a vendor change that hits procurement, finance, legal, and operations; running a quarterly close that coordinates across teams — fall through the gaps in single-function tools and exceed Zapier-class linear automation.
What this is
A workflow-orchestration engagement for cross-functional processes. Three layers:
- Workflow definition. Per-process workflow expressed as a multi-step coordination — which agents do what, in what order, with what decision logic at branching points, with what human-in-loop checkpoints.
- System integration. Connectors into the business's existing systems (CRM, ERP, support, finance, custom). The workflow orchestrates across them; the systems-of-record stay as systems-of-record.
- Observability and recovery. Structured logging, audit trail, idempotent retries, escalation paths. Production-grade across the workflow surface.
How it's built
LLM layer (Claude-class, GPT-class, on-prem where required) for the decision logic. State management in Postgres or a workflow engine (Temporal-class for the complex cases). Integration adapters per system. Observability stack (OpenTelemetry-class).
What you get
- The workflow definitions for the processes you've prioritized.
- The agent orchestration layer.
- System integration with your existing software stack.
- Observability and audit infrastructure.
- Documentation and runbooks for the ops team handing off the workflows.
Engagement is shape, not list.
Length and price are functions of the data and the destination. The shape below is the typical engagement.
- Length
- 8–14 week build · ongoing operation
- Lead
- Bogdan
- Cadence
- Async, weekly
- Bar
- Production
Scoped during the discovery call against the actual data and the operation it integrates with.
Principal engineer. Architecture and most code ships through one keyboard.
Written updates between, calls when the decision needs the room.
Async correctness, capacity under burst, observability at every boundary.
Products this composes with.
Same suite, or vertical-specialized versions in another.
- Same suite · AI Implementation Suite
Document Intelligence Engine
A document-AI pipeline that ingests the business's document corpus, classifies by type, extracts the relevant fields, routes to downstream systems with confidence-graded review queue.
- Same suite · AI Implementation Suite
Customer Support AI
An AI layer over the existing ticket stack — routes tickets by category and urgency, drafts initial responses for routine queries, flags edge cases for escalation, surfaces context from prior interactions and product documentation.
- Same suite · AI Implementation Suite
Finance Operations AI
An AI layer over the finance-ops workflows — invoice ingestion and posting, expense categorization, AP/AR reconciliation, payment matching, fraud-pattern detection. Integrates with the business's accounting system (NetSuite, QuickBooks, Sage, Xero, custom).
What buyers ask about this one.
Why agents instead of just better Zapier?
Linear automation (Zapier, n8n, Make) handles trigger-action chains well. For cross-functional processes with branching logic, error recovery, multi-step coordination across heterogeneous systems, and contextual decision-making, agent-orchestrated workflows handle structure the linear tools miss. We use Zapier-class tools where they fit; we build agent workflows where they don't.
What does 'agent' actually mean in practice?
A workflow component that maintains state, makes context-dependent decisions, and orchestrates calls to other systems and other agents. Concretely: LLM-driven decision logic plus deterministic action execution plus state persistence. The 'agents' aren't autonomous in the alarming-sense; they're scoped to defined workflows with explicit boundaries.
What about reliability and observability?
Every agent step logged with structure. Every decision point auditable. Every external action tracked with success/failure. Failure modes recover gracefully (idempotent retries) or escalate to humans. Production observability is a first-class requirement, not an afterthought.
How do you handle the 'agent goes rogue' concern?
Scoped permissions, audit logs, human-in-loop checkpoints at the steps the business cares about. The agents aren't running arbitrary code; they're executing within a defined workflow with explicit guardrails. The architecture is the safety, not vibes.
Pricing?
Scoped to workflow complexity. Discovery call covers scope.
If the deliverable matches the gap, the next step is one call.
We'll scope length and price against your data and the operation it integrates with. No retainer, no fishing.
Bogdan and team · async-first · OP—2026