Why business outcomes beat technical metrics

Building Smarter Systems with Programmatic Text Extraction Learn how programmatic text extraction boosts speed and accuracy. Read now to build smarter, automated systems for handling digital documents. Read more: https://lnkd.in/gPE23Nfe

In his insightful article, Andrew Green dives into the challenges of evaluating RAG implementations, highlighting common pitfalls like hallucinations and the need for robust evaluation metrics. I found it interesting that improving RAG accuracy can significantly enhance automation workflows. This makes me wonder, how are others in the industry addressing evaluation metrics to optimize their models effectively?

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We’ve reached the limit of manual reliability. Reliability today still depends on manual work. Writing and maintaining tests, investigating incidents, reviewing pull requests for impact, updating metadata, enforcing naming and tagging conventions, and tuning performance and cost. As data platforms grow, this work grows faster. Teams fall behind not because they lack rigor, but because the work scales faster than humans can. The gap is structural. So reliability has to become continuous, automated, and proactive. Not a periodic cleanup sprint, but a system that prevents reliability debt from piling up as the platform evolves. Ella, our network of AI agents, continuously monitors data products, identifies issues, fixes problems, improves test coverage, enriches metadata, and optimizes performance so teams can focus on driving impact rather than maintenance. Now imagine manually tracing the downstream impact of a tiny column rename in this thing. Go ahead. We’ll wait :)

Why You Should Break Your ML Pipelines on Purpose. Traditional monitoring won't catch feature drift or data quality issues. Chaos engineering helps you find hidden issues before they cause damage.

The Two-Speed Myth (Part 2) Time. Signals. Ownership. In Part 1 we framed the problem and anchored on Contract Stability. Here we continue with architectural patterns that let different cadences move safely without breaking each other. B) Temporal Design (How Different Clocks Coexist) Consistency boundary: what must be atomic (uniqueness, payment auth) vs what may be eventual (projections, analytics, loyalty) with apology flows. Temporal contracts: staleness budgets, compensation windows, idempotency keys, version windows. Design: fast path + accurate path; sagas coordinate business time, not just retries. Toolbox: queues, outbox, snapshots, deterministic replay, backpressure, dead-letter. Reads and writes can live on different clocks without blocking each other. C) Telemetry Over Meetings (Shared Signals)   Publish a telemetry contract: trace_id, correlation_id/causation_id, domain entity, change cause, contract version, error taxonomy, freshness SLOs. Make observability the translation layer: dashboards and alerts reflect contract health, not meeting notes. If leaders cannot see constraint movement, they govern as if physics were optional. D) Governance as Code (With Evolution Workflow) Gates in pipelines: contract diff, consumer tests, canary, bake, auto-rollback, kill-switches. Evolution workflow: propose intent → auto impact → steward decision → record as code (version window, deprecation date, exception ID, compensation plan) → enforce in CI/CD → audit trail. Risk is throttled, not frozen. E) Governing Seams, Not Silos (Socio-Technical) Stream-aligned teams own flows and contracts. Platform provides the contract layer as a service. Enabling teams accelerate adoption. Federated governance: providers and consumers evolve canonical contracts together. Conway note: if you cannot name the contract, you will staff the meeting. Anti-Patterns Translator squads as a permanent fix. Digital veneers over legacy cores. API freeze as strategy. Sync RPC coupling across domains. Governance by meeting instead of signal. The Partial Adoption Trap An unenforced contract is worse than none. It manufactures deterministic-in-dev, non-deterministic-in-prod failures. First investment: a CI/CD blocker that fails builds when provider code or consumer tests diverge from the registry. Contract-as-Code is non-negotiable. Next 30 Days 1. Map top seams: owners, SLIs, cadence. 2. Stabilize one contract: versioning, CDC, consumer tests in CI. 3. Publish a telemetry contract; start a seam scoreboard. 4. Add outbox/queue + snapshot to one critical flow. 5. Start CIx reporting; disclose window and sample. Closing Insight Two-speed was a governance story that hardened into architecture. Flip it. Let architecture enable many cadences through contracts, time, and telemetry. Coherence beats speed in isolation. If you cannot measure it, you cannot keep it. — Find this valuable? 👤 Follow // ♻️ Repost // 💬 Comment // 👍 Like // 📌 Save

PIKE-RAG is an advanced Retrieval-Augmented Generation framework that combines specialized knowledge extraction with structured reasoning to deliver highly accurate, context-aware answers. It goes beyond traditional RAG by decomposing complex tasks into logical steps and leveraging domain-specific data for precision. Designed for industrial and enterprise use cases, PIKE-RAG enables reliable decision-making in environments where accuracy and compliance are critical.

Learn why starting your observability strategy with service-level objectives (SLOs) simplifies data collection, reduces costs and aligns tech with business goals.

You've seen this pattern before. Pick a "flexible" framework. It handles everything. Promises to scale. Six months later, you're debugging orchestration logic at 2am, wondering why a simple API call needs three layers of abstraction. ‼️ Welcome to the Complexity Tax. Building one AI agent is easy. Running dozens together reliably in production is where most teams hit a wall. You move from prototype to production and suddenly face problems the framework didn't prepare you for: → ⚠️ Context fragmentation -Agent A flags elevated creatinine. Agent B approves a nephrotoxic drug because kidney issues aren’t in its context window. → 🌀 Hallucination propagation - One agent generates a plausible patient ID. Downstream agents consume it. The fabricated ID becomes ground truth. → 🕵️ Audit complexity - Multi-agent failures require forensics. Which agent made the decision? What information was available? How did interactions influence the outcome? → 🔐 Access control failures - A hallucinated patient ID bypasses your security. The agent doesn’t know it crossed a boundary. → ⚙️ Scaling bottlenecks - One query spawns three agent calls. Another spawns thirty. Your auto-scaling breaks down when workload is non-deterministic by design. These aren't hypotheticals. They're the technical debt most multi-agent frameworks pass on to you. At Corti, we build specialized infrastructure that solves these problems: continuous context, programmatic guardrails, parameter-level provenance, independent scaling layers. Swipe through 👇 Full breakdown: https://lnkd.in/eyTCK66t

⚙️ 𝗕𝗹𝘂𝗲𝗽𝗿𝗶𝗻𝘁 𝗳𝗼𝗿 𝗦𝗲𝗹𝗳-𝗧𝘂𝗻𝗶𝗻𝗴 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 We used to tune systems manually; analyzing logs, tracing latency spikes, and tweaking parameters after each incident. Now, the architecture itself can tune back. The next evolution is self-tuning systems, platforms that observe, decide, and correct without human intervention. Think autoscaling, self-healing, and adaptive load management; powered by telemetry, feedback, and policy. Here’s the blueprint. 👇 🧠 1️⃣ 𝗧𝗲𝗹𝗲𝗺𝗲𝘁𝗿𝘆 𝗜𝗻𝗴𝗲𝘀𝘁𝗶𝗼𝗻—𝗠𝗮𝗸𝗲 𝗘𝘃𝗲𝗿𝘆 𝗦𝗶𝗴𝗻𝗮𝗹 𝗖𝗼𝘂𝗻𝘁 Capture rich, contextual data—traces, metrics, logs, and deployment metadata. Stream continuously into a scalable data pipeline that preserves order and timing. Structured telemetry is the foundation for situational awareness and self-correction. 🔍 2️⃣ 𝗔𝗻𝗼𝗺𝗮𝗹𝘆 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻—𝗙𝗶𝗻𝗱 𝗦𝗶𝗴𝗻𝗮𝗹 𝗶𝗻 𝗡𝗼𝗶𝘀𝗲 Combine statistical baselines with adaptive models that detect drift and seasonality. Score anomalies by confidence and impact, not just deviation. The goal: fewer false positives, faster insight, and early intervention. 🔄 3️⃣ 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸-𝗗𝗿𝗶𝘃𝗲𝗻 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 𝗣𝗹𝗮𝗻𝗲 — 𝗖𝗹𝗼𝘀𝗲 𝘁𝗵𝗲 𝗟𝗼𝗼𝗽 Ingest signals → evaluate → act. The control plane orchestrates actuators—routing, scaling, throttling, and circuit breaking. Each action runs through safety checks and rollback paths. Automation that’s auditable and reversible. 🧭 4️⃣ Policy-Driven Correction—Autonomy with Guardrails Codify rules like: “Don’t reroute more than 10% of traffic without validation.” “Never relax trading limits below safe thresholds.” Use policy-as-code frameworks to ensure decisions remain explainable and compliant. 💡 𝗜𝗻 𝗧𝗿𝗮𝗱𝗶𝗻𝗴 𝗼𝗿 𝗔𝗜𝗢𝗽𝘀 𝗖𝗼𝗻𝘁𝗲𝘅𝘁𝘀 When latency surges or load shifts, a self-tuning system can rebalance flow, isolate noisy clients, or adjust concurrency—in seconds, not hours. That’s how uptime becomes intelligence, not luck. Self-tuning isn’t magic. It’s engineering that listens. Telemetry → Detection → Control → Policy. Close the loop—and your systems start learning faster than your operators. What’s one parameter you’d trust your system to tune automatically? #AIOps #SelfTuning #SystemDesign #Observability #PolicyAsCode #Automation #TradingSystems #EngineeringLeadership #Autoscaling