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Fundamentals of Agentic Software Engineering NEW EDITION: A Practical Guide to LLM Agents, Tool Use, Memory, and Multi-Agent Architecture
GMD 7480
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- A step that succeeds ninety-nine times out of a hundred sounds dependable. Chain thirty of those steps inside a single agent run and dependable is no longer the right word, because reliability multiplies rather than averages.That arithmetic is why so many agent projects look impressive in a demonstration and behave unpredictably in production. Teams are being asked to design, ship and answer for software whose behaviour cannot be fully specified in advance: the author enumerates the action space, but the policy that selects from it is produced at runtime by a statistical model. Most available material sits at one of two extremes. It either explains how language models are trained, which does nothing to help you bound the damage a run can do, or it walks through one vendor's toolkit, which changes faster than any team can standardise on it. The questions in between go unanswered. How long should a loop run? What happens to accumulated state when the context window fills? Which tool calls are safe to retry? What evidence will an assessor ask to see?This book answers those questions by treating an agent as what it is: unusual software, and still software. Sixteen chapters build the discipline from stated assumptions, deriving each result rather than asserting it, with the intermediate steps left in place.What you will be able to do:Model the execution loop as a state machine and set a horizon that doubles as a blast-radius boundAllocate a context budget, choose between compaction and retrieval, and recognise when added context stops helpingSpecify tools as contracts, classify their side effects, and scope capability so a wrong selection stays survivableReason about retry, compensation and checkpointing when the failing step has already changed the worldTrace a failed run back to its cause, and build an evaluation suite whose graders you have actually validatedDecompose cost per completed task and find where growth turns quadraticAssemble the evidence chain, assurance case and documentation an assessment will ask forThe chapters cover the execution model, the model substrate, context engineering, tool interfaces, memory, planning, the runtime harness, multi-agent coordination, reliability, evaluation, observability, security, cost and latency, governance, and integration into an existing delivery lifecycle. Worked examples show every calculation and assumption. Practice problems close each chapter with answers worked in full. Notation is fixed throughout and collected in an appendix, alongside evaluation rubric templates and a standards control-mapping table.The material is deliberately vendor-neutral, and the book is willing to say what is not known: why published benchmark figures are frequently not comparable, and why the major governance frameworks, finalised before autonomous multi-agent deployment scaled, still cover orchestration and delegated decision-making incompletely.It is written for practising engineers, architects and technical leads accountable for a system that includes agents, and for the upper-level student who wants the subject taught as engineering rather than speculation. Every chapter states its prerequisites.Open the first chapter and build a design method you can defend: one that specifies an agentic system, bounds what it can do, measures whether it works, diagnoses it when it does not, and documents it well enough to answer for it.
| Publisher | Independently published |
| Publication date | 9 Sept. 2026 |
| Language | English |
| Print length | 464 pages |
| ISBN-13 | 979-8172866883 |
| Item weight | 1.31 kg |
| Dimensions | 21.59 x 2.67 x 27.94 cm |
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Fundamentals of Agentic Software Engineering NEW EDITION: A Practical Guide to LLM Agents, Tool Use, Memory, and Multi-Agent Architecture
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GMD 7480
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