Insights
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The real cost of putting AI into production: why your bill grows 30x between pilot and scale (and how to contain it)
An inventory of the 7 anti-patterns that drive up the GenAI bill on the move to production (context bloat, over-augmented RAG, looping agents, uncapped retries, unbounded CoT, regenerated embeddings, no prompt caching), the metric that matters more than cost/token (cost per useful task completed), 5 engineering circuit breakers ready to implement (per-feature budget, token cap, agentic iteration cap, retry sampling, cost audit in CI) and a large/small/on-prem model decision table by workload type. Two illustrative cases with the before-and-after math: −77% on the monthly bill without touching accuracy.
Claude Managed Agents: the make/buy dilemma isn't about cost (and your CFO is looking at it wrong)
Managed Agents matches or wins on monthly TCO in 3 of 5 modeled workloads and loses by less than 7% in the other two, setup amortization included. The $0.08/hour fee represents only 6-14% of the total cost in high-concurrency workloads. But migrating a customer-facing workload out of MCA costs around €16,500 (11 engineer-weeks) — that's the data point no one is looking at. Complete model published (auditable xlsx), the 4 make-or-buy questions, an empirical bake-off of 200 executions and a hybrid routing policy to decide with data.
How to choose an AI partner in 2026: five criteria that don't appear in your RFP
The AI-native vendor ecosystem has gone through accelerated consolidation (Accenture-Keepler, Bluetab-IBM, Synergic-Telefónica Tech) and with 40%+ of AI projects failing according to Gartner, traditional RFPs no longer predict success. Five criteria that do: the senior/junior ratio of the assigned team, the ability to reject poorly defined use cases, real vendor neutrality, delivery traceability, and team stability over 12-24 months. Includes what to demand in writing when your partner has just been acquired and how each archetype fits (global integrator, boutique, platform).
Compliance-First AI Design: how to build agents that pass audit
3.5 months before the EU AI Act's high-risk obligations come into force, most AI agents deployed in banking, insurance, health or pharma aren't designed to pass audit. Compliance-first AI design translates the 9 requirements of Regulation (EU) 2024/1689 into a 5-layer architecture, maps SDD to Annex IV and details the 8-week plan to reach August 2 with the evidence layer, human oversight and technical documentation ready.
SDD + Agentic Orchestration: the combination that closes the Anthropic Managed Agents gap
On April 8, 2026 Anthropic launched Claude Managed Agents: sandboxing, orchestration and persistent sessions as a managed service. What the official documentation acknowledges is that prompt engineering, tools, context strategy and guardrails remain the developer's responsibility. This is the architecture that combines SDD as the policy layer with Managed Agents as the execution layer, and why the mid-market teams that adopt it will extract 3-5x more value than those who treat the product as a shortcut.
AI agents in production: the quality gap no one measures (and how to close it)
Observability isn't quality. The 6 dimensions a CTO must measure before going to production, the LLM-as-judge pattern for automatic evaluation, Sentygent as a quality-monitoring tool and how to connect development specs with evaluation in production.
Unlock your AI potential: why winning teams redesign processes before buying tools
Adopting AI with lukewarm policies is no longer enough. 6 patterns that separate leaders from laggards, the uncomfortable questions a CTO must ask today, and a 4-phase method to redesign the organization without stalling operations. The result: an AI-accelerated team that turns speed into a compounding advantage.
Agentic RAG: when your internal documents become your best asset
The difference between traditional RAG and Agentic RAG isn't marginal. Use cases in knowledge management, customer support and R&D. Step-by-step architecture with indexing, retrieval and validation agents. Accuracy, latency and satisfaction metrics. Real ROI against simple chatbots: 60-75% sustained adoption vs 15-25%.
Why most LLM projects fail (and how to turn them into systems that actually work)
95% of AI pilots deliver no P&L impact, according to MIT. 90% of employees use AI on their own, but 68% don't tell anyone. The problem isn't the technology: it's treating LLMs as individual tools instead of systems integrated with context, flow and control.
RAG in production: common mistakes that drain your AI budget
Retrieval without curation, no re-ranking and ignoring when to scale to agentic RAG: the three mistakes that consume the most budget in RAG implementations. A guide with hybrid architecture, benchmark tools and an ROI matrix.
MVP vs Specification: how Spec-Driven Development corrects the "Quick Ship" bias
80% of software rework comes from poorly defined requirements. SDD flips the order: lightweight specifications before the MVP cut sprints from 7 to 3 and eliminate destructive iterations once you integrate agentic AI. A matrix of when SDD is positive ROI vs overhead.
Proprietary vs. open source LLMs in 2026: an enterprise decision guide
From $0.014 to $30 per million tokens: the price range between LLMs has never been so wide. But the performance gap between open source and proprietary has narrowed to 10%. The decision is no longer technical: it's strategic. A 4-axis framework for CTOs who need to decide without paralysis.
From tasks to workflows: AI agents already run complete multi-stage processes
57% of companies already use AI agents to run multi-stage workflows, and 81% plan to tackle more complex cases in 2026. But moving from isolated tasks to complete processes requires clear specifications, structured context and an oversight model that most teams don't yet have.
74% aspire to revenue with AI. Only 20% achieve it. The Deloitte report explains the gap.
Deloitte's State of AI in the Enterprise 2026 reveals a 54-point gap between aspiration and real result in AI revenue. Only 34% reimagine their business, only 1 in 5 has governance for agentic AI, and the skills gap is treated with training when it should be treated with a redesign of work systems.
Skills for AI agents: a practical guide for development teams
Skills are the most-used extension point in tools like Claude Code. But their flexibility makes it hard to know what works. A guide with the lessons from Anthropic's team, 5 proven patterns, anti-patterns to avoid, and how to move from loose instructions to capabilities that scale with your team.