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About Me


This blog is written by Poovannan Rajendran, an account and transformation leader with 20+ years across technology and commercial and specialty insurance. Based in the UK, he shares practical engineering playbooks on AI agents, MCP, security, governance, and delivery in regulated environments.

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...
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AI Agents and MCP in Production: A Practical Architecture Pattern

A practical architecture for building AI agents with MCP, including boundaries, observability, and failure handling. AI agents are moving from demos to production systems, and MCP is quickly becoming a common protocol for tool and context integration. This guide covers a practical baseline architecture. Why this matters now As of 2025-2026, MCP support and agent workflows have expanded across major ecosystems, and teams need interoperable patterns rather than provider lock-in. Baseline architecture Orchestrator layer : plans tasks, manages tool calls, and handles retries. Model layer : reasoning/generation model with explicit prompt contracts. MCP tool layer : context servers for docs, repos, tickets, and internal systems. Policy layer : security rules, redaction, and allowed-tool boundaries. Observability layer : traces, token costs, tool latency, failure telemetry. Key design rules Treat MCP servers as untrusted inputs unless explicitly verified. Whitelist to...
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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...
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