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AI Agents Development

Stage: Services

Build goal-directed, tool-using agents that operate safely within your data, systems, and controls.

10 min readLast updated: Q3 2026

Key Takeaways

LLM Frameworks / Tools
Task Decomposition & Orchestration
Define the Job Decision Rights

§01Technology Stack

LLM Frameworks / Tools

LangChain LlamaIndex OpenAI Functions Anthropic Tool Use

RAG / Graph Retrieval

Graph-RAG

Vector Databases / Embeddings

Pinecone Weaviate Redis

Orchestration / Workflow

Celery Kubernetes Argo

AI Safety / Guardrails

Guardrails.ai

§02Agents Built for the Enterprise

Task Decomposition & Orchestration

Tooling rail: planners • multi-agent workflows • retries Edge: Deterministic fallbacks to prevent loops and stalls.

Tooling & API Integration

Tooling rail: function calling • REST/GraphQL • RPA hooks Edge: Principle-of-least-privilege tokens and audit logs.

Memory & Retrieval (Graph-RAG)

Tooling rail: vector DBs • graph stores • hybrid search Edge: Σ-Graphion™ graph-aware retrieval reduces drift/hallucination.

Evaluation & Guardrails

Tooling rail: prompt tests • content filters • red teaming Edge: EthicΞense™ policy-as-code + Responsible-AI dossier.

Cost, Latency & Routing

Tooling rail: model routers • caching • streaming Edge: Neuro-Quantis™ inference FinOps with budgets and caps.

Operations & Observability

Tooling rail: traces • token logs • canary releases • rollback Edge: Qμβrix™ SLOs for throughput, error rates, and spend.

§03Methodology

Define the Job Decision Rights

CRF: context, risks, functions.

Prototype Safely Guardrails

Sandbox, tool tests, red team.

Scale to Ops Ops

Monitoring, SLOs, runbooks.

§04Agents that obey rules—and deliver value

Planner/orchestrator patterns
Graph-RAG memory and retrieval
Policy-as-code safety & attestations
End-to-end observability
End-to-end observability

§05Assurance Strip

Assurance Strip

Tool Permissions: Managing access controls and user privileges for AI tools and platforms to ensure security and compliance
Retrieval Verification: Validating the accuracy, relevance, and reliability of data or information retrieved by AI systems
Cost Caps: Defining upper limits on cloud, compute, and operational expenses to maintain financial control
Audit Trails: Keeping detailed logs of actions, decisions, and data usage for traceability and regulatory compliance

Boardroom Kit

Agent Safety Checklist: Step-by-step guide to ensure AI agents operate safely, ethically, and within defined boundaries
Tool Permission Matrix: Structured mapping of user roles and access levels for AI tools to enforce security and compliance
Retrieval Test Pack: Set of test cases and procedures to validate the accuracy and reliability of AI retrieval systems
Ops Runbook Template: Standardized template for documenting operational procedures, incident response, and maintenance workflows

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