Automation Practice

AI Agents & Automation.

Task-scoped autonomous agents wired into SAP, Oracle and Salesforce — with signed tool contracts, policy guardrails, human-in-the-loop review and full replay.

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Practice
Automation Practice
Delivery
Riyadh · London · Lahore
Model
Fixed-scope · Retainer
Timeline
6–16 weeks / phase
01 — What we do

Three pillars that carry the practice.

01

One workflow per agent

Purpose-built agents that own a single business process end-to-end — not general chatbots pretending to be workers.

02

Deep ERP integration

Deployed inside SAP S/4HANA, Oracle Fusion and Salesforce with typed function contracts and idempotent side-effects.

03

Guardrails & handoff

Policy engine, approval gates, budget ceilings and clean escalation to humans on any ambiguity or out-of-policy action.

“
We ship this practice as a small, opinionated system — running infrastructure, evaluation harnesses, and the rituals that make leadership trust the numbers.
02 — Capabilities

What we build inside this practice.

Every engagement is scoped as a small system, not a slide deck. You get running infrastructure, documentation, and the metric-store hooks to measure it.

Scope an engagement
01Agent design & tool-contract authoring
02SAP · Oracle · Salesforce integration
03Approval & escalation workflows
04Human-in-the-loop review UI
05Cost, latency & token budgeting
06Prompt & policy eval harness
07Observability, tracing & replay
08Safe-deploy & rollback pipelines
Reference architecture

How the pieces move.

Triggerqueue · webhook · cronPlangoal · policy · budgetActsigned tool callsReviewHITL · approvalsAudittrace · replay
03 — In production

Numbers from the field.

83%
Tier-1 tickets deflected

IT support queue with an agent that owns triage, resolution and knowledge-base updates.

6.4×
Faster PO reconciliation

SAP MM 3-way match owned by an agent with signed BAPI contracts.

$1.2M
Annualised savings

Single procurement agent, first 12 months in production.

04 — The problem we solve

Chatbots don't move the needle. Agents that own work do.

A generic copilot bolted onto ServiceNow saves seconds. An agent that owns 3-way PO matching inside SAP MM — with signed BAPI contracts, approval gates, and clean handoff on ambiguity — closes the month faster. We build the second kind: narrow, auditable, and wired into the systems of record.

1
Workflow per agent — no scope creep
100%
Actions signed, logged and replayable
<2s
Median tool-call latency in production
0
Silent failures — every escalation reaches a human
05 — How we deliver

A methodology we've run — not a slide.

Every engagement follows the same phased spine so leadership always knows what ships in the next two weeks.

  1. 01
    Phase 1

    Workflow selection

    Score candidate workflows on volume, variance, ROI and audit requirements. Pick one to own.

    Workflow scorecardSuccess metricsGuardrail spec
  2. 02
    Phase 2

    Tool contracts

    Author typed, idempotent tool contracts against SAP / Oracle / Salesforce APIs.

    OpenAPI specBAPI/REST wrappersSandbox test suite
  3. 03
    Phase 3

    Agent + evals

    Build the planner, policy engine, and eval harness. Ship behind a feature flag.

    Agent runtimeEval datasetShadow-mode traces
  4. 04
    Phase 4

    Rollout

    Progressive rollout with HITL, budget ceilings, and rollback triggers on drift.

    HITL review UIObservability dashRunbook
06 — Reference stack

Vendor-neutral. Battle-tested.

Agent runtimes
LangGraphOpenAI Agents SDKAnthropic ClaudeVertex AI Agent BuilderSemantic Kernel
Integration
SAP BAPI/ODataOracle Fusion RESTSalesforce Bulk APIMuleSoftn8nTemporal
Observability
LangSmithLangfuseArize PhoenixOpenTelemetryDatadog LLM
Policy & safety
RebuffNeMo GuardrailsPresidio (PII)OPA policy-as-code
07 — What you get

Concrete deliverables.

  1. 01Workflow scorecard + selected agent charter
  2. 02Typed tool contracts with sandbox tests
  3. 03Production agent runtime with policy engine
  4. 04Eval harness with regression dataset
  5. 05Human-in-the-loop review UI
  6. 06Observability dashboards + trace replay
  7. 07Runbook, rollback plan and on-call handoff
  8. 0830-day post-launch performance review
08 — Buyer questions

What leadership actually asks.

Is this just RPA with an LLM sprinkled on top?

No. RPA scripts break on any UI change. Our agents call typed APIs against systems of record, plan under a policy engine, and escalate cleanly. There's no screen-scraping.

How do you prevent hallucinated actions?

Tool contracts are typed and validated. The agent cannot invoke a tool with malformed arguments, and every side-effect requires an approval token from either policy or a human reviewer.

What happens on ambiguity?

The agent stops, logs its reasoning, and hands off to a human via the review UI. No silent failures — that's a hard invariant of the runtime.

Which LLMs do you use?

Model-agnostic. We benchmark GPT, Claude, Gemini and open-weight 8–70B models per workflow, and often mix — a small model for routing and a frontier model for planning.

Can it run air-gapped?

Yes. We deploy vLLM / TGI with quantised open-weight models on-prem for regulated workloads. See our Edge Inference practice.

Ready to scope this into your stack?

Book a working session with a lead architect.

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