C — Architecture, lifecycle and execution diagrams | Agentic AI — Chapter 1: Introduction and Foundations

Detailed Chapter 1 of the Agentic AI learning track: all 15 foundation topics, architecture diagrams, runnable Python, Java and React examples, solved questions, 15 exercises, a complete mini-project, interview preparation and revision. Chapters 2–20 are upcoming.

Prerequisites: Beginner-friendly theory. Basic familiarity with functions helps for the Python lab; the runnable example uses only the standard library. No provider credentials are required. This is Chapter 1 of a planned 20-chapter track, not a completion claim for the entire course.
Section C — Architecture and Diagrams
Read the following diagrams as trust and data-flow maps. Arrows indicate messages or control transitions, not automatic grants of authority. The controller owns budgets and stop conditions, while the tool gateway and individual tools enforce access. The decision component may use an LLM in a later chapter; the demonstration here uses a scripted policy so expected behavior can be reproduced without a network.
Component architecture
User / UI
    | goal + credentials
    v
Authenticated API --> validated goal
    |
    v
Run controller <----> Run state / audit events
    | limited context
    v
Decision policy (mock or model)
    | proposed action
    v
Schema + authorization + budget checks
    | allowed request only
    v
Read-only tool registry --> Authorized job catalogue
    | observations
    +--------------------> Controller --> Result evaluator --> User
The API derives identity from its authentication mechanism and validates the user request. The controller constructs context from that goal and permitted observations. A decision policy returns a proposal such as search_jobs with a query. Before execution, code checks that the tool is registered, arguments are valid and budget remains. The search adapter receives verified identity rather than trusting an identity copied from the model response. Its result is an observation, which the controller records and evaluates before another decision.
Lifecycle flowchart
Receive goal
  -> Authenticate and validate
  -> Initialize run with deadline and step budget
  -> Cancelled or over budget? -- yes --> Finish with explicit status
  -> Ask decision policy
  -> Final proposal? -- yes --> Validate evidence --> Complete / partial
  -> Validate tool and arguments
  -> Authorization denied? -- yes --> Record denial --> Stop or bounded recovery
  -> Execute tool with timeout
  -> Record observation and update state
  -> Evaluate progress
  -> Decide next step (bounded loop)
A final proposal still needs validation: it may contain unknown job IDs or unsupported claims. Denial must not be turned into an empty successful search. Tool failures should remain distinguishable from zero matches, and repeated identical failures should not trigger unlimited retries. In a production implementation, an overall deadline and provider cancellation complement a step limit because a single tool can take a long time.
Sequence: one successful read-only run
User          Controller       Decision policy      Tool gateway       Catalogue
 | goal ---------> |                  |                    |                 |
 |                 | context -------> |                    |                 |
 |                 | <--- search proposal                  |                 |
 |                 | validate + authorize ----------------> |                 |
 |                 |                  |                    | query --------> |
 |                 | <------------- authorized observations <--------------- |
 |                 | observations --> |                    |                 |
 |                 | <--- final IDs   |                    |                 |
 |                 | verify IDs + completion criteria       |                 |
 | <--- result ----|                  |                    |                 |
Execution trace and invariants

["Step","Input","Check","Result"]
["1","Goal: find Python jobs","Authenticated user, bounded query","Run initialized"]
["2","search_jobs proposal","Tool allowlist, argument type, budget","Tool allowed"]
["3","Catalogue response","Only visible records for verified user","Observed job IDs stored"]
["4","Final candidate IDs","IDs must exist in observed results","Evidence-backed result"]
["5","Any terminal outcome","Status remains success, failed or budget_exhausted","No fabricated completion"]
An invariant is a property that must remain true throughout execution. Here every returned job must be visible to the user, every tool must be registered and every recommendation must refer to an observed result. These are executable checks, not suggestions in a prompt. When extending the architecture, ask whether a new memory store, cache or asynchronous worker can break an invariant even if the original search tool remains correct.

Chapter 1 — Introduction to Agentic AI