Skip to main content

AI Agents

AI Agents hub


Use this page to navigate all agent-related implementation guides.


Recommended reading order:


1) AI Agents and MCP in Production

https://technical-reference.blogspot.com/2026/02/ai-agents-and-mcp-in-production.html


2) Vibe Coding with Guardrails

https://technical-reference.blogspot.com/2026/02/vibe-coding-with-guardrails-ship-faster.html


3) Model Comparison Framework

https://technical-reference.blogspot.com/2026/02/model-comparison-framework-for-ai.html


What you will find on this site:

- Agent architecture patterns

- Tool and protocol boundaries

- Reliability and observability guidance

- Production rollout checklists


Bookmark this page as your working index for AI agent delivery.


Comments

Post a Comment

Popular posts from this blog

AI Evaluation Harness: From Prompt Tests to Production Release Gates

A practical framework for building an AI evaluation harness that links test quality to release decisions and operational confidence. Evaluation harnesses turn subjective model quality into measurable release criteria. Combine functional, safety, latency, and cost checks into one pipeline. Block releases when critical thresholds are missed, even under delivery pressure. If your AI release decision is based on a demo, you are not releasing engineering software; you are releasing a hope strategy. A proper evaluation harness creates repeatable evidence for quality, safety, and cost trade-offs. Prerequisites Versioned prompts and model configuration. Representative test dataset by use case. CI/CD pipeline with artefact retention. Clear service-level objectives for latency and reliability. Evaluation layers 1) Functional correctness Golden set response checks. Tool invocation correctness. Schema compliance for structured outputs. 2) Safety and policy Prompt in...
Post a Comment
Read more

AI Security and Ethics Checklist for Engineering Teams

A practical pre-release checklist for AI features covering security, misuse risk, transparency, and governance. Shipping AI features without security and ethics checks creates hidden operational risk. Use this checklist before each release. 1) Data and privacy Confirm data minimisation in prompts and context. Remove secrets and personal data from logs. Enforce retention windows for model inputs and outputs. Validate third-party processor boundaries. 2) Security controls Restrict tool permissions by role and environment. Validate all tool outputs against strict schemas. Add prompt-injection defences for external content. Require approval gates for high-impact actions. 3) Safety and misuse Define clear disallowed use cases. Add risk prompts for potentially harmful requests. Add user-visible warnings for uncertain outputs. Add abuse monitoring and escalation paths. 4) Transparency and trust Disclose where AI assistance is used. Explain known limitations...
Post a Comment
Read more

AI Agent Failure Modes: Detection, Triage, and Recovery Runbook

A practical incident runbook for AI agent systems, covering common failure modes and response actions that reduce production impact. Most agent incidents are predictable: tool misuse, context drift, and weak guardrails. Build a failure taxonomy and link each class to detection and recovery playbooks. Track MTTR and recurrence to continuously harden your agent platform. Agent systems do not fail in one way. They fail across planning, context, tool invocation, and execution boundaries. Without a clear runbook, teams lose time arguing about symptoms instead of restoring service. This guide provides an operating model you can implement immediately. Prerequisites Incident severity model (SEV1, SEV2, SEV3). On-call owner for agent platform. Baseline observability for prompts, tool calls, and outcomes. Rollback path for model and policy configuration. Failure taxonomy 1) Intent misclassification The agent chooses the wrong plan for a valid request. Signals: - Wrong w...
Post a Comment
Read more