The BraivIQ AI Playbook - Engineering-Grade Guides For Production AI
Deep, technical reference guides on building production AI: agent architecture, RAG pipelines, enterprise integration, deployment, evaluation, guardrails, trading systems and ROI. Written by the engineers who build BraivIQ agents for financial firms, with sources cited.
Engineering Tracks
Agentic AI · Workflow Automation · RAG & LLM Engineering · AI Integration · Deployment & Production · AI Strategy & ROI · Trading · Case Studies.
Who It Is For
Engineering and product teams building real AI systems - covering production agent architecture, retrieval-augmented generation, Model Context Protocol integration, LLM deployment and observability, evaluation harnesses, guardrails and prompt-injection defence, machine learning in trading systems, and measuring AI ROI.
Featured
In agentic AI architecture for a financial firm, no tool call that changes a record should run because the model, or a message, said so. Every call passes a deterministic gate outside the model, and anything consequential waits for an approval that a named person signed for those exact arguments, with each decision written to a tamper-evident audit log. On 28 September 2026 the UK AI Security Institute reported that GPT-6 Astra, in a simulated evaluation, treated an automated "Please proceed" message as permission to act. Between 10 September and 7 October, Anthropic, LangChain, Pydantic, Google and Microsoft all shipped pre-tool-use controls. This playbook shows the pattern in tested Python.
All playbooks (95)
- Agentic AI Architecture For Approvals You Can Prove: Pre-Tool Gates, Signed Sign-Off And Audit Logs In Python - Agentic AI ·
- Trade Surveillance AI In Code: Triage Alerts With An Analyst In The Loop And Chart The Evidence - Trading ·
- LLM Classification Under The Hood: How The New Decision Models Return Probabilities, And When Not To Use Them - RAG & LLM Engineering ·
- AI Guardrails After The Bank Of England's September Warning: What UK Regulators Now Expect From Teams Building AI Agents - AI Strategy & ROI ·
- T+1 Settlement Automation Inside An Investment Bank: A Worked Example Of What The Dev Team Has To Learn - Deployment & Production ·
- Workflow Automation Architecture For Human Sign-Off: Durable Approval Steps With LangGraph 1.2 In Python - Workflow Automation ·
- MCP Integration After The September 2026 SDK Advisories: Pinned Issuers, Audience Checks And Bounded Sessions In Python - AI Integration ·
- Prompt Injection Defence For AI Agents In Code: Provenance Tracking, Quarantined Readers And Signed Messages - Agentic AI ·
- Who Is This Agent And What May It Do? Agent Identity, Delegated Authorization And The 2026 Protocol Stack In Code - Agentic AI ·
- The Price Of Thinking: Reasoning Effort, Thinking Budgets And How To Spend Reasoning Tokens In Production - A Senior Engineer's Guide - RAG & LLM Engineering ·
- Britain's AI Growth Lab: Regulatory Sandboxes Where UK Developers Can Ship Under Relaxed Rules - And The Copyright Line They Still Cannot Cross - AI Strategy & ROI ·
- Building A Real-Time Alerts Engine For Trading Charts In Code: Evaluating Thousands Of Price And Indicator Conditions On Every Tick - Trading ·
- Pre-Trade Risk Controls And The Kill Switch: What A Trading Firm's Dev Team Must Learn To Build For Algorithmic And AI-Driven Trading - Case Studies ·
- Event-Driven Agents: Webhooks, Queues And Backpressure - Triggering Automations Reliably In Code - Workflow Automation ·
- MCP Tool And Skill Supply-Chain Security: Scoped Sessions, Signed Archives And Digest Verification In Code - AI Integration ·
- Build For Agents, Not Screens: Agent Experience (AX) And Agent-First API Design In Code - Agentic AI ·
- Inside LLM Inference Serving: Prefill, Decode, KV Cache, Continuous Batching, Speculative Decoding And Disaggregation - Explained For Senior Engineers - Deployment & Production ·
- Britain Built The Evals Framework The World Uses: Inspect, The AI Security Institute, And Why UK Developers Should Be Proud - And Use It - AI Strategy & ROI ·
- Building An Incremental Technical Indicator Engine In Code: Streaming RSI, MACD, Bollinger Bands And VWAP Without Recomputing The World - Trading ·
- AI Trade Surveillance: What A Trading Firm's Dev Team Must Learn To Detect Spoofing And Layering On Streaming Order Data - With Alerts A Regulator Can Read - Case Studies ·
- Building Real-Time Voice Agents In Code: Speech-To-Speech Vs Cascaded Pipelines, Latency Budgets, Turn Detection And Barge-In - AI Integration ·
- Building A PII Detection And Redaction Pipeline In Code: Rules, NER, LLM Classification And Auditable Masking Before Anything Leaves The Building - Workflow Automation ·
- When There Is No API: Computer-Use Agents Are The New Integration Layer For Legacy Systems - How To Architect Them In Code - AI Integration ·
- Stop Parsing JSON With Regex: Structured Outputs And Constrained Decoding In Production - A Senior Engineer's Guide - RAG & LLM Engineering ·
- Britain Is Opening Everything: Smart Data Schemes, The FCA's Open Finance Roadmap, And What UK Developers Get To Build - A Pro-UK Read - AI Strategy & ROI ·
- Regression-Proof Your Agents: Why Agent Evaluation In CI/CD Is 2026's Most Under-Built Layer - And How To Build It In Code - Deployment & Production ·
- Building A Bar Aggregation Engine In Code: From Raw Ticks To Time, Tick, Volume And Renko Bars For Multi-Timeframe Charts - Trading ·
- The Mainframe Problem: What A Bank's Dev Team Must Learn To Modernise COBOL With AI Agents - Without A TSB-Style Meltdown - Case Studies ·
- Agent Interoperability In Code: The A2A Protocol, Agent Cards And How Agents From Different Vendors Actually Talk To Each Other - Agentic AI ·
- The SLM Shift: Why Senior Engineers Are Right-Sizing Models In 2026 - A 27B Model On One GPU, 10-30x Cheaper, Behind Your Own Firewall - RAG & LLM Engineering ·
- Intelligent Document Processing Pipelines In Code: Ingest, Classify, Extract, Validate, And Route Only The Doubt To Humans - Workflow Automation ·
- Britain's Cyber Bill Puts The Duty On You, The AI Deployer - Not The Model Vendor: What UK Dev Teams Must Build - A Pro-UK Read - AI Strategy & ROI ·
- Building An AI Candlestick Pattern Recognition Engine In Code: From Rule-Based Detectors To ML Overlays On The Chart - Trading ·
- From Overnight Batch To Intraday: What An Investment Bank's Dev Team Must Learn To Build A Real-Time Risk Engine On Top Of Legacy - Case Studies ·
- The AI Gateway Pattern: Routing, Fallback, Caching And Cost Control Across Multiple Models - The Layer Every Production AI System Now Needs - AI Integration ·
- Agent Skills As Composable Workflow Units: How To Package Automation Into Reusable, Versioned, Testable Capabilities Your Agents Load On Demand - Workflow Automation ·
- Agent Memory Is The New Production Layer: How To Architect What Your AI Agents Remember - In Code - Agentic AI ·
- Agentic RAG In Code: Moving Retrieval Inside The Agent Loop - A Senior Engineer's Guide To 2026's Production Pattern - RAG & LLM Engineering ·
- Securing The AI Coding Pipeline: What Engineering Leaders Must Do When Agents Write The Code - Deployment & Production ·
- Building Execution Algorithms In Code: VWAP, TWAP, POV And Implementation Shortfall From The Ground Up - Trading ·
- The Backtesting Trap: What A Quant Dev Team Must Learn So Their Strategy Does Not Die In Production - Case Studies ·
- What The UK's New Automated-Decision Rules Mean If You Build AI That Decides: DUAA 2025, Articles 22A-22D - A Developer's Pro-UK Read - AI Strategy & ROI ·
- Beyond The Chatbox: Building Generative Agent Interfaces That Show Reasoning, State And Trust - In Code - AI Integration ·
- The Managed Agent Harness Has Arrived: What OpenAI's Agents API, LangGraph 1.2 And CrewAI A2A Mean For How You Architect Production Agents In Code - Agentic AI ·
- MCP Just Went Stateless: Architecting Remote Model Context Protocol Servers As Ordinary HTTP Workloads - AI Integration ·
- Context Engineering In Code: How Senior Engineers Actually Build The Context Window In 2026 - RAG & LLM Engineering ·
- Building A Real-Time Trading Chart Engine In Code: WebGL Candlesticks, Streaming Updates And The Render Loop - Trading ·
- The Market Data Firehose: What An Investment Bank's Dev Team Must Learn To Build A Low-Latency Feed Handler And Order Book In Code - Case Studies ·
- Britain Invented MCP And Let It Go: The Case For A UK Sovereign Open-Source Foundation - A Developer's Pro-UK Read - AI Strategy & ROI ·
- Durable, Crash-Safe Agent Workflows In Code: Architecting Long-Running Automation With LangGraph 1.2 And Checkpointing - Workflow Automation ·
- Spec-Driven Development: Why 2026's Biggest Shift In Building With AI Is Writing The Spec, Not The Prompt - Agentic AI ·
- You Can't Run What You Can't See: LLM And Agent Observability In Production, Explained For Engineering Leaders - Deployment & Production ·
- Britain's AI Growth Zones And Compute Roadmap: What The £68bn Infrastructure Bet Means For UK Developers - An Honest, Pro-UK Read - AI Strategy & ROI ·
- Building An Order And Execution Management System (O/EMS) In Code: The Trade Lifecycle From Capture To Settlement - Trading ·
- Building A Real-Time Trading Blotter And P&L Dashboard In Code: Live Positions, Orders, Fills And Profit-And-Loss That Never Lie - Trading ·
- The Regulatory Reporting Nightmare: What A Trading Firm's Dev Team Must Learn To Automate Compliance Reporting And Data Integration With AI - Case Studies ·
- Multi-Agent Orchestration In Production: What Actually Survived - Coordination, Failure Modes And The Build-Versus-Buy Decision - Workflow Automation ·
- Sandboxing AI Agents In Production: The Runtime Security Playbook For Agents That Browse, Call APIs, Write Files And Push Changes - Deployment & Production ·
- Running AI Inside The Firewall: What A Trading Firm's Dev Team Must Learn To Deploy LLMs In A Locked-Down, Air-Gapped Regulated Environment - Case Studies ·
- Agent Evaluation Is Now Its Own Discipline - And A Commercial Category: What Senior Engineers And CTOs Need To Know - Agentic AI ·
- Britain Bets On Open-Source AI: What DSIT's Compute-For-Builders Push And The AI Security Institute Mean For UK Developers - An Honest, Pro-UK Read - AI Strategy & ROI ·
- Building A Smart Order Router In Code: Scanning Venues, Splitting Orders And Routing For Best Execution - Trading ·
- Visualising Execution Quality In Code: Building Transaction-Cost-Analysis And Slippage Dashboards For Trading - Trading ·
- Before The Tool Call: Pre-Action Authorization And Tool-Call Governance For Production AI Agents - AI Integration ·
- Building A GPU-Accelerated Options Pricing And Risk Engine In Code: Monte Carlo, The Heston Model, And The Rent-Versus-Build Compute Decision - Trading ·
- What A Trading Firm's Dev Team Actually Has To Learn To Ship AI: Legacy Modernisation, The Verification Crunch And Model Risk - A Real-World Playbook - Case Studies ·
- AI Agent Memory Is Now A First-Class Engineering Problem: The Three Tiers Every Senior Engineer And CTO Must Understand - Agentic AI ·
- Visualising The Volatility Surface And The Greeks In Code: 3D Rendering, Interpolation And Real-Time Options Analytics For Trading Interfaces - Trading ·
- Britain's Technology-Agnostic AI Rulebook For Finance: Why The UK's 'No Special AI Law' Approach Is A Quiet Advantage For Developers - An Honest, Pro-UK Read - AI Strategy & ROI ·
- AI SRE Is Here: Building Agentic Incident Response That Cuts Time-To-Mitigation From Hours To Minutes - A Production Engineering Playbook - Deployment & Production ·
- The Quant Research-To-Production Pipeline In Code: Reproducibility, Experiment Tracking And Shipping Trading Signals Without The Gap - Workflow Automation ·
- Building A High-Performance Limit Order Book And Matching Engine In Code: Price-Time Priority, Lock-Free Data Structures And 10M+ Orders Per Second - Trading ·
- AI Coding Agents Are In Production: Why Verification, Not Code Generation, Is Now The Bottleneck - A Playbook For Senior Engineers And CTOs - Agentic AI ·
- Sovereign Compute And The UK's AI Bet: What Britain's Push For Sovereign AI Means For Developers And Enterprises - An Honest, Pro-UK Read - AI Strategy & ROI ·
- Market Data At Scale: Choosing A Time-Series Database And Building A Tick Store For Trading With kdb+, ClickHouse And QuestDB - Deployment & Production ·
- Visualising The Order Book In Code: Depth Charts, Heatmaps And Level-2 Market Data Rendering For Trading Interfaces - Trading ·
- Streaming Market-Data Pipelines In Code: Kafka, Backpressure And Exactly-Once Processing For Trading Automation - Workflow Automation ·
- Building A Market-News And Sentiment RAG Pipeline For Trading Signals In Code: Ingestion, Retrieval, LLM Analysis And The Look-Ahead Trap - RAG & LLM Engineering ·
- Architecting An Agentic Trading System In Code: The Event-Driven Decision Loop, Multi-Agent Design, Backtest-Live Parity And Risk Engine - Trading ·
- AI In Trading Systems: A Technical Playbook For Machine Learning, Backtesting, Execution And Risk Controls In Automated Markets - Trading ·
- Building A Real-Time Trading Chart Engine In The Browser: WebGL Rendering, Streaming Market Data And TradingView-Grade Performance In Code - Trading ·
- The Production Agent Architecture Playbook: Planning, Tool Use, Memory And Guardrails For Agents That Survive Contact With Reality - Agentic AI ·
- Context Engineering For CTOs And Senior Engineers: The Discipline That Quietly Replaced Prompt Engineering - And Now Decides Whether Your AI Works - RAG & LLM Engineering ·
- Building A Production RAG System: The Complete Playbook For Chunking, Retrieval, Reranking And Evaluation - RAG & LLM Engineering ·
- Why Britain Is A Trading-Technology Superpower: How The FCA, London's Market Infrastructure And UK AI Policy Give Developers An Edge - An Honest, Pro-UK Read - AI Strategy & ROI ·
- The MCP Integration Playbook: Connecting LLMs To Your Enterprise Systems Without Building A Dozen Bespoke Bridges - AI Integration ·
- Integrating Trading Infrastructure In Code: FIX, WebSockets, REST Broker APIs And MCP For AI-Native Trading Systems - AI Integration ·
- Deploying LLM Applications To Production: A Playbook For Latency, Cost, Caching And Observability - Deployment & Production ·
- Durable Trade Processing And Reconciliation In Code: Event-Driven Workflows, Idempotency And Durable Execution For The Financial Back Office - Workflow Automation ·
- Choosing An AI Workflow Automation Backbone: n8n, Make, Zapier And Temporal Compared For Real Production Workloads - Workflow Automation ·
- Deploying Low-Latency AI Inference For Trading: The Latency Budget, Hot Path Versus Slow Path, GPU Serving And Colocation In Code - Deployment & Production ·
- The LLM Evaluation Playbook: Moving From Vibe Checks To Real Metrics So You Can Ship AI Changes With Confidence - RAG & LLM Engineering ·
- Guardrails And Safety For Production LLM Systems: Defending Against Prompt Injection, Data Leakage And Unsafe Actions - Deployment & Production ·
- Measuring AI ROI: A CFO-Grade Framework For Proving The Return On Your AI Investment - AI Strategy & ROI ·
- Case Study: How An Agentic Support Assistant Cut Average Handle Time While Improving Resolution Quality - Case Studies ·