What is a good first automation?

Pick a workflow with clear inputs, clear outputs, and a manual baseline you can compare against. Ticket triage, document classification, structured extraction. Skip creative / open-ended workflows for the first ship.

Will the agent replace people?

In practice it replaces the L1 layer of repetitive work and lets the same headcount handle 2–4× the volume. The work that remains is higher-judgement work that is harder to automate and more valuable.

Can we start with one workflow and grow?

That is the recommended pattern. Ship one well-scoped agent end-to-end. Build the shared infrastructure as the second and third agents land — see the Enterprise AI Architecture solution.

Solution

Automate the operations bottleneck.Production agents for triage, support, ERP workflows, and content.

Enterprise automation is where agentic AI pays back fastest: operational triage, customer support, ERP-integrated workflows, content-at-scale pipelines. Concrete inputs, measurable outputs, decisions you can audit. We design, build, and ship these systems end-to-end — and your team owns the code.

Scope an automation engagement See ERP triage case study

2–4×

throughput per operator / reviewer

< $5

average cost per agent-handled decision

6–10 wk

first agent in production

Full

audit trail per decision

Use cases

Operational automations that ship reliably

Ticket triage + L1 support

Classify, contextualise from CRM / ERP, draft response, escalate complex cases with a pre-filled handoff brief.

ERP-integrated customer workflows

Odoo / SAP / NetSuite MCP servers behind a supervisor agent that handles customer queries with full ERP context.

Content + SEO pipelines

Research → outline → draft → review pipelines for teams producing content at scale.

Document processing

Invoice / contract / KYC document extraction with structured outputs and validation.

Sales lead qualification

CRM-integrated qualification agents that score inbound leads, pull context, draft outreach.

Compliance review

Layered-guardrail document review for regulated workflows — see the AI Guardrails solution for the full pattern.

Industries served

IT ServicesERP / Enterprise SoftwareFinancial ServicesHealthcare OperationsReal EstateEdTech

Technology

Enterprise automation stack

Methodology

Delivery process

Workflow audit

Pick the operational workflow with clear inputs / outputs / measurable steps. Skip creative or open-ended workflows for the first ship.

Architecture

Agent role(s), tool registry, integration points, observability, eval plan, audit requirements. Reviewed with your operations and engineering leads.

Implementation

Production-grade code on your stack. Staging environment from week 2.

Parallel-run

Agent runs alongside the existing manual workflow for 1–2 weeks. Outputs compared. Errors triaged.

Handoff

Documentation, eval-set update process, monitoring dashboards. 30/60/90-day check-ins.

Security & scalability

Security & scale for operational agents

Read-only by default

Mutating actions are added deliberately. Where they exist, they go through approval queues or human-in-the-loop gates above defined risk thresholds.

Per-decision audit log

Every agent decision logs inputs, tools called, model used, output, cost, and (where applicable) the human approver.

Cost discipline from day one

Per-workflow cost tracked. Most automations land under $5 per decision in steady state.

Graceful failure

Every agent has a fallback path. If the agent cannot handle something confidently, it escalates with full context — it does not guess.

Integrations

Systems we automate against

Odoo · SAP · NetSuite · Microsoft Dynamics
Salesforce · HubSpot · Zoho · Pipedrive
Zendesk · Freshdesk · Intercom · custom ticket systems
Slack · Microsoft Teams · WhatsApp Business · email
PostgreSQL · MySQL · MongoDB · custom data stores
Stripe · Razorpay · custom payment / billing systems

Business impact

Why operational automation is the highest-leverage start

Operational workflows are where the inputs and outputs are most concrete and the measurements are most defensible. They are also where the manual cost is most visible, which makes the business case easy.

2–4×

throughput per operator

~70%

of L1 cases handled without human touch

< $5

cost per agent-handled decision

6–10 wk

time-to-production

Case studies

How recent engagements actually shipped

ERP / Enterprise Software · 8 weeks discovery → handoff

ERP support triage agent eliminates the Level-1 backlog

Odoo-based ERP partner · Gujarat · ~60 implementation consultants

Problem

Customer support backlog had grown to ~340 open tickets. Level-1 triage took 12–20 minutes per ticket on average, and 35% of tickets were misrouted on first pass — every misroute became a customer-facing escalation churn.

Solution

A supervisor-pattern agent that ingests email and form submissions, classifies the issue, queries the customer's Odoo instance for context (open invoices, recent modules, last login, current contracts), drafts a Level-1 response with the right module screenshots inline, and routes complex tickets to the right consultant with a pre-filled handoff brief.

Claude Sonnet 4.6 (drafting)Custom MCP server: Odoo (read-only customer / order / invoice scope)Supervisor patternPydantic schemas

340 → 18

open L1 backlog within 6 weeks of go-live

~60%

L1 staffing reduction on agent-eligible categories

$2.30

average cost per agent-resolved ticket

8 wks

engagement, discovery to handoff

Read the full case study

Deep dives

Read what we publish on this

Tool Design

Tool descriptions are prompts. Fix the registry, not the agent.

When an agent picks the wrong tool, the registry is broken — not the agent. Three rules I now apply before debugging anything in a multi-tool system: precise names, "when to use" triggers, and a curated load list. Anthropic's new tool-selection telemetry finally puts numbers on what changes accuracy.

Read the post Production

The cheapest LLM call is the one you do not make — GitHub's 19-62% token cut, decoded

GitHub published an instrumented analysis of their agentic CI workflows and reported 19-62% token-cost reductions. The savings are the headline. The technique — pre-agentic data fetching and tool-registry hygiene — is the story most teams will miss.

Read the post Architecture

Claude Opus 4.7's 1M context: when to RAG and when to just stuff it

A million tokens reliably is real now, but it does not retire RAG — it changes the calculus. Cost, latency, recency, and the prompt-cache angle nobody is talking about.

Read the post MCP

MCP 1.0 is here. What changes for the servers you already wrote

The protocol stabilised. Most working servers will keep working. Three places the new spec actually requires changes — auth profile, server registry, streaming-response semantics — with diffs from a real migration.

Read the post

Frequently asked

AI Automation for Enterprises — questions buyers ask

Scope your first operational agent

Bring the workflow you want to automate. We map the agent boundary, the integration points, and the expected cost / throughput in a 60-minute scoping session.

Book an automation scoping call Read the enterprise AI automation pillar

Adjacent

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Topic Pillar