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The Agentic Enterprise: Architecture, Strategy, and the Shift to Autonomous AI: Structuring the Pivot to the Augmentation Economy
Paperback
Series: Designing Autonomous AI Agents, Book 2
Publisher: Independently Published
Published: Dec 26 2025
Pages: 216
Weight: 0.65
Height: 0.46 Width: 6.00 Depth: 9.00
Language: English
The demo worked perfectly. Then you deployed it.
Every enterprise AI team knows this moment. The pilot is impressive. The stakeholders are excited. And then production hits - fourteen-hour sessions drifting off-task, confident hallucinations nobody catches, a legacy sensor feeding bad data into a chain of perfectly logical reasoning steps, and a governance team asking questions you can't answer. The gap between what your AI system can do and what it reliably does is real, it's systematic, and it's costing you more than you think.
This book closes that gap.
Engineering Agentic Reliability is the production-first implementation guide for enterprise AI reliability - grounded in 12,847 real sessions across seven organizations, five industries, and two years of documented failures. Every framework layer, every metric threshold, and every code example in this book was forged in production systems that broke in specific, repeatable ways until the engineering finally got it right.
You will build:
- The Conversational Resilience Framework (CRF) - a five-layer defense-in-depth architecture that catches the failure modes conventional software testing can't: contextual drift, hallucination under uncertainty, tool failure propagation, goal misalignment, and governance opacity
- CDR monitoring with real Python implementations - detect goal drift before it cascades into fourteen hours of wasted work
- Uncertainty-Aware Guidance (UAG) - stop hallucinations at the source with external confidence calibration, not post-generation filtering
- Precision human-in-the-loop governance that scales - not rubber-stamp review queues, but surgical escalation that routes the right decisions to the right humans with the context they need
- Immutable audit infrastructure that satisfies regulatory examiners in financial services, healthcare, and legal - built from the audit requirements that actually showed up in examinations
- Production observability for agentic systems - because traditional APM was never designed for probabilistic, session-spanning, emergent failure
Who this is for: Staff engineers building agentic pipelines. Solutions architects designing multi-agent systems. Engineering managers explaining to a frustrated VP why the pilot system is behaving differently in production. Technical leaders in regulated industries who need AI governance that holds up under scrutiny - not just governance theater.
Domain case studies include: Healthcare clinical decision support (sepsis early warning, medication reconciliation), financial services AML transaction monitoring, manufacturing predictive maintenance at scale, and legal contract review - each with domain-specific CRF configuration, real metric outcomes, and documented failure incidents.
The code is production Python 3.11+. The metrics are empirically calibrated. The failure patterns are real. And the gap between impressive demos and trustworthy production systems is closeable - with the right engineering.
Build the discipline. Close the gap.
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