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RSS FeedWelcome to Byte Journal, where enterprise technology meets practical insights. As a Principal Consultant with extensive experience in digital transformation, I share deep perspectives on enterprise architecture, cloud strategies, and technology-enabled innovation.
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A Prompt Is Not a Permission: Containment for Production Agents
Published: at 04:35 AMIn a 2026 red-teaming competition every one of 13 frontier models fell to indirect prompt injection at least once, and persistence turns low per-attempt risk into near-certainty. Prompt-level guardrails are detectors, not controls: this article maps which containment layer — identity-scoped tool access, kernel sandboxing, inference-boundary guardrails, network-layer protection — stops which failure mode.
Seven in Ten Enterprises Cannot Tell Which Agent Broke Production
Published: at 03:50 AMKore.ai's Agent Productivity Index 2026 found 70% of enterprises could not name the agent responsible for a multi-agent failure, even though most detect that something broke. Attribution is an identity problem: it needs an action record, a dependency graph and a named owner, not another dashboard.
Raising the Autonomy Ceiling: When Can an Agent Ship Without a Human?
Published: at 03:05 AMOnly 5% of enterprises fully trust automated evaluation, yet two-thirds already permit or are building toward agents that act without a human. Autonomy belongs to an action class, not an agent, and the ceiling needs a promotion file with a signature and a demotion trigger.
The EU AI Act Deadline That Actually Applies Now
Published: at 02:20 AMThe Digital Omnibus moved high-risk duties to December 2027, but Article 50 transparency has been enforceable since 2 August 2026. What is live today, what lands on 2 December, and how to spend the deferral on evidence.
Agents Are Software: Give Them a Delivery Pipeline
Published: at 01:35 AMOnly 53% of agent changes travel a standard pipeline, 19% of organisations have a gate that blocks a bad release, and a third have an instant kill switch. What an agent release actually requires.
When the Agent Goes Down, Who Can Still Do the Work?
Published: at 12:50 AMIMDA's updated agentic AI framework and the platform reliability data point at the same gap: organisations are removing the human path faster than they are proving it still works. What a tested manual fallback, a proficiency standard and continuity drills actually require.
Explainability Under Pressure: AI Decisions in Credit, Insurance and Hiring
Published: at 06:50 AMExplainability is an operating obligation, not a model-output feature. How leaders in credit, insurance and hiring make high-impact automated decisions understandable, contestable and defensible before enforcement lands.
Shadow AI Is Not Shadow IT
Published: at 06:05 AMShadow AI is a data-boundary and assurance problem, not just an unmanaged application. Discovery through telemetry, a usable approved path, data-class policy and an agent register are what let leaders decide whether to block, tolerate or enable.
Who Pays When the Agent Is Wrong?
Published: at 05:20 AMAutonomous agents act, but most enterprises have not decided who is accountable when an action is wrong, whether their insurance responds, or what evidence would defend a claim. The fix sits in contracts, controls and policy wordings — before the first material incident.
Prompt, Retrieve or Fine-Tune: How to Choose Where Model Behaviour Lives
Published: at 04:35 AMPrompting, retrieval and fine-tuning are three places to store behaviour, not three ways to get better answers. A decision framework for choosing where a behaviour lives, who maintains it, and what should never be baked into model weights.
Deflection Is Not Resolution: The Real Economics of AI Customer Service
Published: at 03:05 AMContainment and deflection rates flatter AI service deployments because they count contacts that never reached a human, not problems that were solved. Here is how to measure resolution — repeat contacts, escalation cost, wrong-answer cost and churn — and decide whether your service metrics are telling the truth.
Internal Audit Has to Assure Systems That Change Themselves
Published: at 02:20 AMInternal audit was built to test controls that stay still. AI systems change behaviour when a model is upgraded, a prompt is edited or data drifts, so the third line now needs continuous evidence, a model and prompt change register, and a board report that states residual risk rather than a stale pass.
Recent Posts
The Compute Bill Comes Due: Why AI Roadmaps Now Have a Capacity Ceiling
Published: at 01:35 AMModel capability has stopped being the binding constraint on enterprise AI. Electricity, data-centre capacity and regional availability now cap what a roadmap can deliver — so leaders must forecast compute in business terms, rank workloads by value per unit of compute, and run capacity as a portfolio decision.
You Contracted for a Capability, Not a Change Process
Published: at 12:50 AMEnterprises buy AI as a static capability but sign a relationship with a vendor whose model, pricing and terms keep moving. What a 2026 AI contract must specify before signature.
From Pilot Factory to Product Factory: How CIOs Build a Repeatable AI Delivery Engine
Published: at 12:50 AMMost AI programmes are reliable machines for producing pilots. A product factory funds in stages, names an operator before build, demands evidence at every gate and retires weak work.
The Agent Portfolio: Why Enterprises Need Fewer Agents with Clearer Owners
Published: at 08:20 AMAgent governance fails when nobody can list the agents. Portfolio discipline — one purpose, one sponsor, explicit action authority, a real run cost and a retirement trigger for every agent — is the control that makes the rest possible.