ARTlligence — Agentic Mesh. Governance Native. Built for Production.

The Problem

80% Use GenAI.
80% See No P&L Impact.

Enterprises aren't short on AI ambition. They're drowning in AI sprawl — disconnected pilots, duplicated infrastructure, zero shared domain knowledge, no governance. Gartner: 40% of agentic AI projects will be cancelled by 2027 without governance.

🌪️

AI Sprawl

Where most enterprises are stuck

✗Every use case built in isolation — separate models, integrations, governance

✗Domain knowledge recreated from scratch for each pilot

✗No standard protocol for agent-to-agent communication

✗Observability absent — agents running blind, costs invisible

✗Governance bolted on after the fact — or not at all

✗Costs scale linearly: 15 pilots manageable. 50 aren't.

✗Hallucination risk unchecked in production decisions

🕸️

Agentic Mesh

What we build for you

✓Domain knowledge encoded once, shared across all agents

✓Standardized 10-component architecture — consistent, composable

✓Open protocols: MCP (agent↔tools) + A2A (agent↔agent)

✓AgentOps observability native — full trace, cost, audit from Day 1

✓AI governance wired in: ISO 42001 · EU AI Act · NIST AI RMF

✓Costs flatten as use cases compound — second agent is cheaper than first

✓Evaluation gates: hallucination rate tracked, domain accuracy verified

Complete Architecture

The 10-Component Agent Architecture

Every production agent we build implements all ten components. Miss one and your agent is a demo, not a product. Each component is independently auditable, replaceable, and observable.

01 / CORE

🧠

Agent (LLM Core)

The LLM decision engine. Model selection, prompt engineering, context management. LLM-agnostic design — swap models without rewiring the system.

Claude · GPT-4o · Gemini

02 / PLANNING

📋

Planner

Task decomposition, goal sequencing, tool selection per step. Hybrid: LLM handles ambiguity; deterministic rules handle regulated decisions.

LangGraph · ReAct · CoT

03 / INTEGRATION

🔧

Tools (MCP)

Standardized API access via Model Context Protocol. Agents discover tools dynamically. 50+ managed MCP servers. Domain-specific tool registries per vertical.

MCP · REST · GraphQL

04 / PERSISTENCE

💾

Memory

Working memory (context window, compressed) + long-term memory (vector store, episodic). Memory tiering controls cost. Temporal versioning for domain rules.

Pinecone · Redis · PostgreSQL

05 / KNOWLEDGE

📚

Retrieval (RAG)

Domain-tuned hybrid retrieval: semantic embeddings + keyword + structured filters. Citation tracking on every chunk. Sub-200ms latency budgets.

Weaviate · LlamaIndex · HyDE

06 / CONTROL

⚙️

Orchestration

Durable workflow engine with stateful directed graph, error recovery, retry logic, parallel execution, circuit breakers. Human-in-loop gates at configurable checkpoints.

Temporal · LangGraph · ADK

07 / RUNTIME

🔄

Execution Loop

Observe → Think → Act → Evaluate. Hard loop limits prevent runaway costs. Checkpoint after every Act step. Cost tracked per iteration.

ReAct · MRKL · Tree-of-Thought

08 / SAFETY

🛡️

Guardrails

Policy enforcement, PII detection, hallucination blocking, domain-specific validation rules. Wraps every agent output before it becomes an action. Kill switch integration.

NeMo · Lakera · Custom Rules

09 / QUALITY

📊

Evaluation

Task success rate, hallucination rate, tool accuracy, domain accuracy, latency vs SLA, cost per execution. Continuous eval — not a quarterly review.

Langfuse · LangSmith · RAGAS

10 / VISIBILITY

🔭

Observability

Full prompt logging (PII masked), tool call tracing, reasoning trace, output + eval results, cost attribution. OpenTelemetry foundation. Compliance audit export.

OTel · Langfuse · Grafana

🔗

Integration Layer: MCP (agent ↔ tools) + A2A (agent ↔ agent)

Open standards donated to the Linux Foundation. Microsoft, Google, AWS, Salesforce, SAP, ServiceNow all running A2A in production. No vendor lock-in by design.

AgentOps · MLOps · AIOps

If You Can't See It,
You Can't Trust It

Most AI demos skip observability. In production it's the difference between an agent you trust and one you fear. We deploy the full observability stack from Day 1 — tracing, cost metering, guardrail events, audit trail, multi-tenant dashboards.

🔍

Execution Tracing

Full dynamic call tree per agent task. Every span, every hop, every LLM call, every tool invocation — with timestamps and latency. OpenTelemetry foundation.

OTel spanscall graphlatency P99

💰

Cost Metering

Token consumption attributed at every level — per LLM call, per agent, per task, per session, per customer tenant. Cost attribution tree decomposed correctly.

input tokensoutput tokenscost/tx

📝

LLM Call Logging

Full prompt + response captured (PII masked before storage). Model version, temperature, parameters, latency per call. Framework-agnostic via LLM proxy layer.

prompt logPII maskmodel ver

🛡️

Guardrail Event Capture

Every output flagged, every rule triggered, every modification made, every human escalation — logged with rule ID, rationale, and outcome. Exportable for compliance audit.

guard eventsrule triggersaudit export

🧪

Evaluation Pipeline

Continuous evaluation — not a quarterly review. Task success rate, hallucination rate (RAGAS), tool accuracy, domain accuracy. Agents that test agents.

hallucination %domain accRAGAS

💾

Memory Observability

What was written to long-term memory, what was retrieved, how memory is growing per tenant. Flags agents accumulating context without compression.

mem writesretrieval costgrowth rate

ARTlligence AgentOps — Live Trace Stream

09:14:02.001 INFO [orchestrator] tx_id=bos_9821 initiated — deal: unit_407_buyer_ramesh

09:14:02.140 INFO [rera_agent] RAG retrieval — query: GujRERA proj HPPL-2024-1198 chunks=6 latency=142ms

09:14:02.290 COST [rera_agent] loop_1: input=3,240tok output=480tok cost=$0.0091

09:14:02.410 OK [rera_agent] compliance: PASS — project registered, possession date valid, RERA cert attached

09:14:02.501 INFO [finance_agent] GST calc — unit_value=₹82,00,000 slab=5% (under-construction)

09:14:02.610 OK [finance_agent] TDS 194-IA: ₹82,000 (1%) — 26QB prefilled — deterministic engine, no LLM

09:14:02.780 INFO [doc_agent] generating allotment_letter.pdf — template: standard_v4

09:14:02.940 GUARD [guardrail] output validated — amount_check: PASS clause_check: PASS citation_check: PASS

09:14:03.020 EVAL hallucination_score: 0.00 domain_accuracy: 1.00 task_success: PASS

09:14:03.101 COST tx_total: input=9,840tok output=2,210tok cost=$0.031 latency=1.1s

09:14:03.200 OK tx_id=bos_9821 COMPLETE — docs: 3 generated, 0 escalations, audit_log: written

09:14:03.310 INFO [orchestrator] memory write — buyer_profile updated, deal_state persisted to long-term store

MLOps

Model Lifecycle, End-to-End

Every model in production is versioned, evaluated, monitored, and continuously improved. We don't deploy models. We operate them.

📥

Data Prep

Corpus design, chunking, embedding, freshness policy

🏋️

Fine-Tune

LoRA, domain adaptation, instruction tuning per vertical

🧪

Evaluate

RAGAS, hallucination rate, domain accuracy gates

🚀

Deploy

Blue/green, canary, shadow mode — zero-downtime rollout

📡

Monitor

Drift detection, latency SLA, cost per inference

🔄

Retrain

Feedback loops, failure analysis, continuous improvement

AI Governance

Governance Native,
Not Bolted On

Gartner warns 40% of organizations will face security incidents from unauthorized AI agents by 2030. In regulated industries, demonstrable control over automated systems is not optional — it's the license to operate. We wire governance in at Day 1.

ISO 42001

AI Management System (AIMS)

The international standard for AI management systems. We implement the full AIMS lifecycle: context of organization, risk assessment, objective setting, operational controls.

Clause 6: AI risk assessment and treatment

Clause 8: AI system impact assessment

Clause 9: Performance evaluation — monitoring, audit

Annex A: Organizational controls for responsible AI

Annex B: AI system supply chain controls

EU AI Act

Risk-Based Compliance

Full risk classification per Article 9: unacceptable risk prohibition, high-risk system controls, transparency obligations. Conformity assessments and post-market monitoring.

Article 9: Risk management system — documented and live

Article 13: Transparency and provision of information

Article 14: Human oversight — enforced at orchestration layer

Article 17: Quality management system alignment

High-risk system registration and conformity documentation

NIST AI RMF

AI Risk Management Framework

Structured implementation across all four NIST AI RMF core functions. Trustworthiness dimensions mapped to observable system properties with measurement plans.

GOVERN: AI risk policies, roles, accountability structures

MAP: Context identification, risk categorization

MEASURE: Risk analysis, tracking, evaluation metrics

MANAGE: Treatment priorities, response plans, residual risk

Trustworthiness: valid, reliable, safe, fair, explainable

Governance-as-Code

Enforced at Runtime

Compliance rules are not in a manual — they're in the system, enforced at runtime, version-controlled, and auditable. Every guardrail rule has a state machine. Immutable audit log.

NeMo Guardrails with domain-specific rulesets

Lakera Guard for prompt injection + PII protection

Kill switch integration — instant agent pause capability

Human-in-loop gates at every high-stakes decision point

Temporal signals for durable HITL approval workflows

20 AI Operating Systems

Every Sector. Production-Ready.

20 AI Operating Systems built on the 10-component architecture — each with a live dashboard and full business case. These are reference implementations that become your client's production system.

Every OS is a reference implementation — not a demo

Built on the 10-component architecture. Your sector's system becomes production-ready in 12 weeks.

Start a Discovery Call See How We Build →

Prototype → Production

The 7-Layer Platform
That Makes It Real

The 20 OS products show what's possible. This is the production platform that turns any of them into a system a CTO will stake their operations on — with durable orchestration, evaluation gates, full observability, and enterprise-grade security built in.

7-Layer Production Stack

L7

Enterprise Presentation

React · Next.js · SSO

L6

Agent API Gateway

FastAPI · RBAC · Rate Limiting

L5

Workflow Orchestration

Temporal · LangGraph · ADK

L4

Agent Runtime

NeMo Guardrails · RAGAS · Token Budgets

L3

Model & Tool Layer

Claude · Gemini · MCP Connectors

L2

Data & Integration Layer

Kafka · PostgreSQL · SAP · Salesforce

L1

Observability Foundation

Langfuse · OpenTelemetry · Audit Log

What separates demos from production

Temporal.io orchestration: Durable workflows that survive crashes. Human-in-the-loop gates that pause and wait for real approvals. Every decision replayable.

RAGAS evaluation gates: Faithfulness threshold >0.92 blocks every deployment. 5% of live outputs evaluated continuously. Quality degradation caught in 24h, not at client review.

MCP enterprise integrations: Live connections to SAP, Salesforce, SharePoint, ServiceNow — not mock data. Real workflows on real enterprise systems.

LLMOps observability: Every LLM call traced — input, output, model, tokens, cost, latency. Cost attribution per agent, per workflow, per tenant. Real-time anomaly alerts.

12-week delivery: Weeks 1–2: discovery + data architecture. 3–4: platform foundation. 5–8: agent build with quality gates. 9–10: load testing. 11–12: production launch.

Open Full Platform Architecture ↗ Book a Technical Deep-Dive 14 sections · Temporal HITL patterns · RAGAS eval pipeline · cost architecture · 12-week roadmap

Services

How We Work With You

Three engagement models — from rapid proof of value to full enterprise platform delivery. All engagements include governance, evaluation, and observability from Day 1.

🔭

AI OS Implementation

12 weeks · £200K–£400K

Take any of the 20 AI OS products and deploy it in your environment, connected to your data, configured to your governance requirements. The fastest path from "we saw the demo" to "we're running it in production."

Choose your sector OS — 20 reference implementations available

Weeks 1–2: discovery, data architecture, integration feasibility

Weeks 3–4: platform foundation — Temporal, Langfuse, MCP connectors

Weeks 5–8: agents built and evaluated — golden dataset quality gates

Weeks 9–12: load testing, canary deployment, production launch

🏗️

Custom Agentic Platform

16–24 weeks · £500K–£1.2M

Bespoke multi-agent system designed for your specific use cases — built on the 10-component architecture. For organisations with complex workflows, proprietary data models, or regulatory requirements that need custom agent design.

Deep discovery: stakeholder workshops, data archaeology, risk mapping

Custom agent contract design — inputs, outputs, evaluation criteria

Enterprise integrations: SAP, Oracle, bespoke ERPs via MCP

Full governance framework: ISO 42001, EU AI Act, NIST AI RMF

30-day post-launch hypercare + 12-month improvement retainer

🧪

Proof of Value Sprint

4 weeks · £40K–£80K

Not a demo — a working agent connected to your live data, evaluated against your domain, with full observability. Designed to give your CTO the evidence they need to make an informed build decision, not just a business case.

2 agents built — highest-value use case identified in Week 1

Connected to 1–2 live data sources via MCP

Golden dataset built with your domain experts (30–50 cases)

RAGAS evaluation score delivered — quality proven, not asserted

Architecture decision document: what full build requires

Book Discovery Call

Let's Talk About
Your AI OS

Tell us your sector and your biggest operational pain point. We'll come prepared with a relevant OS demo, an integration assessment, and a realistic path to production.

Full Name

Company

Work Email

Sector

Biggest operational pain point you want AI to solve

From AI Sprawlto Agentic Mesh

80% Use GenAI.80% See No P&L Impact.

The 10-Component Agent Architecture

If You Can't See It,You Can't Trust It