Written by School of Core AI·Reviewed by School of Core AI Technical Training Team·Last reviewed 2026-09-08·Version 1.0

PROJECT AND PORTFOLIO GUIDE

Agentic AI Projects: Production Systems for Engineers

Move beyond chatbot demos. Choose projects that prove state handling, tool use, evaluation, recovery and safe execution.

Filter projects by your engineering context:

Experience

Role

Pattern

Primary evidence

8 projects match your filters

1. Tool-Using Research AgentFoundation
One model, one retrieval tool, structured output. Prove state handling, grounding, and output validation before adding complexity.
single-agent
2. Stateful Customer-Support AgentIntermediate
A single agent with persistent state across sessions. Demonstrates checkpointed state, tool permissions, and failure recovery.
single-agent
3. Agentic RAG Knowledge AnalystIntermediate
An agent that decides when to retrieve, reranks results, and grounds answers in cited sources. Proves retrieval decisions and grounding checks.
agentic-rag
4. Multi-Agent Operations SystemAdvanced
A supervisor and specialist agents that handle operations tasks. Proves handoff coordination, shared-state synchronization, and trajectory evaluation.
multi-agent
5. MCP-Based Enterprise Tool AgentAdvanced
An agent that connects to enterprise systems through MCP servers with least-privilege tool boundaries. Proves tool security and audit logging.
mcp
6. Browser Agent With Human ApprovalAdvanced
An agent that automates browser interactions with in-loop approval gates for sensitive actions. Proves approval design and action verification.
browser
7. Agent Evaluation and Control PlatformAdvanced
A platform that traces, evaluates, and governs agent executions. Proves trajectory evaluation, release gates, and observability controls.
agentops
8. Production Agent System CapstoneAdvanced
A full production agent system integrating state, retrieval, tools, evaluation, and deployment. Proves end-to-end system ownership.
single-agentagentic-ragmulti-agentmcpbrowseragentops

A strong Agentic AI project proves more than model output. It shows how the system manages state, chooses tools, handles permissions, recovers from failure and measures task success. Start with one controlled tool-using agent, then add retrieval, memory, specialist handoffs, evaluation and deployment only when each layer solves a real requirement.

What Makes an Agentic AI Project Portfolio-Worthy?

A demo shows that a model can produce output. A portfolio-worthy project shows that an engineer can control a system that uses a model. The difference is in five areas:

State management: The project persists state across steps and sessions, checkpoints after each step, and recovers from failure without restarting from zero.
Tool boundaries: Every tool has a schema, a permission scope, and an audit log. The system validates arguments before execution and runs tools within least-privilege boundaries.
Evaluation: The project measures task completion, trajectory quality, tool-call correctness, state integrity, safety compliance, latency, and cost — not just final-answer quality.
Failure recovery: The project handles at least three failure cases: tool timeout, state corruption after retry, and approval denial. Each failure has a documented recovery path.
Deployment: The project runs in a containerized environment with a documented deployment plan — scaling, secrets, monitoring, and rollback — even if the deployment is local.

A project that lacks these five areas is a demo. A project that has them is evidence of system ownership.

Choose a Project by Your Current Engineering Level

Pick a project that matches what you can already build. A project that is too simple proves nothing; a project that is too complex proves nothing because it does not work. The table below maps your level to what you can build, what you should prove, and what to avoid.

Project scope by engineering level
Level	What you can build	What to prove	What to avoid
Foundation	A single tool-using agent with one retrieval tool, structured output, and a step budget.	State handling, grounding, output validation, and basic evaluation on a small query set.	Multi-agent handoffs, long-term memory, and complex retrieval pipelines before the single-tool loop is solid.
Intermediate	A stateful single agent with persistent state, 2–3 tools, or an agentic RAG system with multi-source retrieval.	Checkpointed state, tool permissions, failure recovery, grounding checks, and evaluation on 20+ tasks.	Multi-agent coordination and production deployment until the single-agent state and retrieval loop is reliable.
Advanced	Multi-agent operations, MCP-based enterprise tools, browser agents with approval, an AgentOps platform, or a production capstone.	Handoff coordination, least-privilege tool design, approval gates, trajectory evaluation, and a deployment plan.	Adding agents, tools, or features that do not serve the core task. Each addition must justify its coordination and complexity cost.

Eight Agentic AI Projects to Build

Each project below lists the real problem, why an agent is justified, when a deterministic workflow would be better, the system architecture, agent state, tools and permissions, retrieval and memory, human approval points, failure cases, evaluation metrics, deployment surface, portfolio evidence, difficulty and prerequisites, and a “do not overbuild” note. No project includes complete production code — a small pseudocode fragment or interface contract is sufficient for a portfolio.

1. Tool-Using Research Agent

Level: Foundation · Pattern: Single-agent

Real problem: A user needs answers grounded in a specific knowledge base with citations, not free-form generation.
Why an agent is justified: The system must decide when to retrieve, which source to query, and when to stop — decisions that depend on the query.
When a deterministic workflow would be better: If the same fixed query always hits the same fixed source, a keyword search pipeline is simpler and more reliable.
System architecture: One model, one retrieval tool, a structured output schema, and a single execution loop with a max-step budget.
Agent state: Current query, retrieved documents so far, step count, and a stop signal. Checkpoint after each step.
Tools and permissions: One retrieval tool scoped read-only. No write access. Validate all tool arguments before execution.
Retrieval / memory: Per-session retrieved snippets. No long-term memory in the foundation version — keep the surface small.
Human approval points: Not required for a read-only research agent. Log every retrieval call for audit.
Failure cases: Retrieval returns nothing, retrieval returns irrelevant results, model fabricates a citation, loop exceeds step budget.
Evaluation metrics: Answer groundedness (citation match), retrieval precision, step count, latency, cost per query.
Deployment surface: Containerized API behind a rate-limited endpoint. No persistent state needed beyond the session.
Portfolio evidence: README with problem statement, architecture diagram, evaluation results on 20+ queries, trace example, and failure cases handled.
Difficulty and prerequisites: Foundation. Prerequisites: basic Python, API calls, structured output parsing, a vector or keyword search index.
Do not overbuild: Do not add memory, multi-agent handoffs, or a vector database until the single-tool loop is solid and evaluated.

interface ToolUsingResearchAgentState {
  sessionId: string
  stepCount: number
  stepBudget: number
  checkpoint: CheckpointId
  // project-specific fields elided
}

2. Stateful Customer-Support Agent

Level: Intermediate · Pattern: Single-agent with state

Real problem: Support conversations span multiple turns and sessions. The agent must recall prior context and act within policy.
Why an agent is justified: The system must interpret varied user messages, decide between answering and escalating, and use tools within policy boundaries.
When a deterministic workflow would be better: If the support flow is a fixed decision tree (refund → verify → approve), a rules engine is more reliable than an agent.
System architecture: One model, persistent session state, 2–3 tools (lookup, ticket create, policy search), and checkpointed state.
Agent state: Session ID, conversation history, user profile, open tickets, policy context, step count, approval flags.
Tools and permissions: Account lookup (read), ticket creation (write, scoped), policy search (read). Each with a schema and permission scope.
Retrieval / memory: Conversation history per session. Policy documents retrieved on demand. Optional long-term memory for user preferences — govern with a retention policy.
Human approval points: Human approval before any write action that modifies a ticket state or issues a refund.
Failure cases: State corruption after retry, stale checkpoint, tool timeout, policy violation by the model, context window overflow.
Evaluation metrics: Task resolution rate, escalation accuracy, state integrity after recovery, tool-call correctness, latency, cost.
Deployment surface: Durable state store (database or Redis), containerized agent service, audit log for every tool call.
Portfolio evidence: Architecture diagram showing state store, trace of a multi-turn session, evaluation results, and a recovery demonstration.
Difficulty and prerequisites: Intermediate. Prerequisites: the foundation project, persistent state design, tool permission boundaries, basic evaluation.
Do not overbuild: Do not add multi-agent handoffs or a complex memory hierarchy until the single-agent state loop is reliable and evaluated.

interface StatefulCustomerSupportAgentState {
  sessionId: string
  stepCount: number
  stepBudget: number
  checkpoint: CheckpointId
  // project-specific fields elided
}

3. Agentic RAG Knowledge Analyst

Level: Intermediate · Pattern: Agentic RAG

Real problem: Analysts need answers across multiple knowledge sources with citations, follow-up retrieval, and source-aware reasoning.
Why an agent is justified: The system must decide when to retrieve, which source to query, whether to rerank, and when to retrieve again — all query-dependent.
When a deterministic workflow would be better: If one source always answers the query with a single retrieval, a standard RAG pipeline is simpler and cheaper.
System architecture: One model, multiple retrieval tools (per source), a reranker, a grounding checker, and a multi-step loop with a retrieval budget.
Agent state: Query, retrieved chunks per source, rerank scores, grounding verdict, step count, retrieval budget remaining.
Tools and permissions: One retrieval tool per source, each scoped read-only. A rerank tool and a grounding-check tool. No write access.
Retrieval / memory: Per-session retrieved chunks. Optional long-term memory for analyst preferences — govern with selective retention.
Human approval points: Not required for read-only analysis. Log every retrieval and rerank call.
Failure cases: Retrieval returns irrelevant chunks, rerank fails, grounding check fails, loop exceeds retrieval budget, model hallucinates a citation.
Evaluation metrics: Answer groundedness, citation accuracy, retrieval precision per source, rerank quality, step count, latency, cost.
Deployment surface: Containerized API, vector or keyword indices per source, evaluation dataset of multi-source queries.
Portfolio evidence: README with source list, architecture diagram, evaluation on 30+ multi-source queries, trace example, and failure cases handled.
Difficulty and prerequisites: Intermediate. Prerequisites: the foundation project, multi-source retrieval, reranking, grounding checks, evaluation.
Do not overbuild: Do not add multi-agent or a complex memory store until the agentic retrieval loop is grounded and evaluated across sources.

interface AgenticRAGKnowledgeAnalystState {
  sessionId: string
  stepCount: number
  stepBudget: number
  checkpoint: CheckpointId
  // project-specific fields elided
}

4. Multi-Agent Operations System

Level: Advanced · Pattern: Multi-agent

Real problem: Operations tasks span monitoring, diagnosis, and remediation — each with distinct tools and expertise.
Why an agent is justified: The system must route subtasks to specialists, coordinate shared state, and handle parallel diagnosis — coordination that a single agent cannot manage cleanly.
When a deterministic workflow would be better: If the operations flow is a fixed runbook (alert → check metric → restart), a deterministic workflow engine is more reliable.
System architecture: A supervisor agent routes subtasks to specialist agents (monitor, diagnose, remediate). Shared state store. Trajectory tracing across agents.
Agent state: Supervisor state: task queue, handoff log, active specialists. Specialist state: subtask, tools, results, step count.
Tools and permissions: Monitoring APIs (read), diagnostic queries (read), remediation actions (write, scoped, approval-gated). Each tool has a permission scope and audit log.
Retrieval / memory: Shared incident context. Optional long-term memory for prior incident patterns — govern with retention and poisoning controls.
Human approval points: Human approval before any remediation action. The approval prompt shows the supervisor's diagnosis and the specialist's proposed action.
Failure cases: Handoff lost, shared-state conflict, specialist timeout, cascading failure, inconsistent diagnosis across specialists, loop budget exceeded.
Evaluation metrics: Task resolution rate, handoff correctness, shared-state integrity, trajectory quality across agents, safety compliance, latency, cost.
Deployment surface: Durable state store, containerized agent services, message queue for handoffs, audit log for every tool call across agents.
Portfolio evidence: Architecture diagram showing supervisor and specialists, trace of a multi-agent incident, evaluation results, and a recovery demonstration.
Difficulty and prerequisites: Advanced. Prerequisites: the intermediate projects, multi-agent handoff design, shared-state synchronization, trajectory evaluation.
Do not overbuild: Do not add more specialists than the task requires. Each specialist must justify its coordination cost with a concrete benefit.

interface MultiAgentOperationsSystemState {
  sessionId: string
  stepCount: number
  stepBudget: number
  checkpoint: CheckpointId
  // project-specific fields elided
}

5. MCP-Based Enterprise Tool Agent

Level: Advanced · Pattern: MCP

Real problem: Enterprise users need an agent that can act across multiple internal systems through a consistent, governed interface.
Why an agent is justified: The system must select the right tool across systems, compose multi-step actions, and stay within least-privilege boundaries.
When a deterministic workflow would be better: If the user always performs the same fixed sequence across the same systems, a scripted integration is simpler and safer.
System architecture: One agent connected to multiple MCP servers. Each server exposes narrow tool operations. The agent validates arguments and logs every call.
Agent state: User identity, active session, available MCP tools, tool-call history, approval flags, step count.
Tools and permissions: MCP tools scoped per server. Read tools (queries, lookups) and write tools (create, update) with least-privilege credentials and audit logs.
Retrieval / memory: Per-session tool results. Optional long-term memory for user preferences — govern with retention and poisoning controls.
Human approval points: Human approval before any write tool. The approval prompt shows the tool, arguments, target system, and permission scope.
Failure cases: MCP server unavailable, tool argument invalid, permission denied, credential exposure, untrusted result re-injected into context, loop budget exceeded.
Evaluation metrics: Task completion, tool-call correctness, permission compliance, audit completeness, latency, cost.
Deployment surface: Containerized agent, MCP servers per system, credential vault, audit log, evaluation dataset of multi-system tasks.
Portfolio evidence: Architecture diagram showing MCP servers and trust boundaries, trace of a multi-tool task, evaluation results, and security review notes.
Difficulty and prerequisites: Advanced. Prerequisites: the intermediate projects, MCP protocol, least-privilege tool design, argument validation, audit logging.
Do not overbuild: Do not expose broad database or shell access through MCP. Each tool should be a narrow, purpose-built operation.

interface MCPBasedEnterpriseToolAgentState {
  sessionId: string
  stepCount: number
  stepBudget: number
  checkpoint: CheckpointId
  // project-specific fields elided
}

6. Browser Agent With Human Approval

Level: Advanced · Pattern: Browser

Real problem: Users need an agent that can automate browser interactions on systems without APIs — forms, portals, internal tools.
Why an agent is justified: The system must interpret page state, decide which elements to interact with, and handle dynamic content — decisions that depend on the page.
When a deterministic workflow would be better: If the page structure is stable and the flow is fixed, a deterministic browser automation script is more reliable than an agent.
System architecture: One agent, a browser automation tool (Playwright or similar), a page-state observer, and in-loop approval gates for sensitive actions.
Agent state: Current URL, page snapshot, action history, pending approval, step count, action budget.
Tools and permissions: Browser actions: navigate, click, type, read. Sensitive actions (submit, delete) are approval-gated. Each action is logged with a screenshot.
Retrieval / memory: Per-session action history. No long-term memory in the core version — keep the attack surface small.
Human approval points: Human approval before any submit, delete, or payment action. The approval prompt shows the intended action, target element, and a screenshot.
Failure cases: Page structure changes, element not found, action timeout, model selects wrong element, approval denied, action budget exceeded.
Evaluation metrics: Task completion, element selection accuracy, approval-gate compliance, action correctness, latency, cost.
Deployment surface: Containerized agent, headless browser, screenshot storage, audit log, evaluation dataset of browser tasks.
Portfolio evidence: README with problem statement, architecture diagram, trace of an approved action, evaluation results, and failure cases handled.
Difficulty and prerequisites: Advanced. Prerequisites: the intermediate projects, browser automation, page-state interpretation, approval-gate design.
Do not overbuild: Do not give the agent unrestricted browser access. Scope tools to specific domains and gate every sensitive action behind approval.

interface BrowserAgentWithHumanApprovalState {
  sessionId: string
  stepCount: number
  stepBudget: number
  checkpoint: CheckpointId
  // project-specific fields elided
}

7. Agent Evaluation and Control Platform

Level: Advanced · Pattern: AgentOps

Real problem: Agent teams need a platform that traces, evaluates, and governs every execution — the control plane that production requires.
Why an agent is justified: The platform itself may use agents to classify failures, suggest evaluation improvements, or generate regression tests — but the core is a control system, not a task agent.
When a deterministic workflow would be better: If tracing and evaluation rules are fixed, a deterministic observability pipeline is simpler and more reliable.
System architecture: A tracing collector, an evaluation engine, a policy/guardrail layer, a release-gate evaluator, and a dashboard. Optional classifier agent for failure triage.
Agent state: Trace store, evaluation datasets, policy rules, release decisions, cost budgets, alert state.
Tools and permissions: Trace ingestion (read), evaluation run (read), policy update (write, approval-gated), alert dispatch (write, scoped).
Retrieval / memory: Historical traces and evaluation results. Govern with retention policies — this store grows fast.
Human approval points: Human approval before any policy change or release-gate override. Log every policy decision.
Failure cases: Trace ingestion drops data, evaluation dataset is stale, policy rule is too strict or too loose, alert storm, cost budget exceeded.
Evaluation metrics: Trace coverage, evaluation pass rate, policy violation rate, alert precision, release-gate accuracy, platform latency, cost.
Deployment surface: Containerized services, trace store (database or object storage), evaluation dataset store, dashboard, audit log.
Portfolio evidence: Architecture diagram showing the control plane, trace example, evaluation results, and a release-gate demonstration.
Difficulty and prerequisites: Advanced. Prerequisites: the intermediate projects, tracing design, evaluation datasets, policy/guardrail implementation, release gates.
Do not overbuild: Do not build a full APM platform. Focus on agent-specific controls: trajectory evaluation, tool-call tracing, and release gates.

interface AgentEvaluationandControlPlatformState {
  sessionId: string
  stepCount: number
  stepBudget: number
  checkpoint: CheckpointId
  // project-specific fields elided
}

8. Production Agent System Capstone

Level: Advanced · Pattern: All (integrated)

Real problem: Demonstrate end-to-end system ownership: a production agent that integrates state, retrieval, tools, evaluation, and deployment.
Why an agent is justified: The capstone exists to prove you can combine every layer into a controlled, evaluated, deployable system — not to showcase a single technique.
When a deterministic workflow would be better: A capstone should include a clear statement of which parts are agentic and which parts are deterministic. Do not make everything an agent.
System architecture: A single primary agent with state, retrieval, permissioned tools, and an evaluation/control plane. Optional specialist for one justified subtask.
Agent state: Session state, checkpointed after each step, with idempotency keys and versioning for concurrent access.
Tools and permissions: Scoped tools with schemas, permission boundaries, audit logs, and at least one approval-gated write action.
Retrieval / memory: Per-session retrieval with grounding checks. Optional long-term memory with a documented retention and poisoning-control policy.
Human approval points: Human approval for high-impact or irreversible actions. The approval prompt shows intent, arguments, and consequences.
Failure cases: At least three failure cases handled: tool timeout, state corruption after retry, and approval denial. Document recovery for each.
Evaluation metrics: Task completion, trajectory quality, tool-call correctness, state integrity, safety compliance, latency, cost — measured on a real evaluation dataset.
Deployment surface: Docker-based local deployment with a documented production deployment plan (scaling, secrets, monitoring, rollback).
Portfolio evidence: Full README: problem, architecture diagram, tool permissions, evaluation results, failure cases, deployment plan, and a retrospective on what you would do differently.
Difficulty and prerequisites: Advanced. Prerequisites: all prior projects or equivalent experience. This is a synthesis project, not a starting point.
Do not overbuild: Do not add features that do not serve the core task. The capstone proves control and ownership — not feature count.

interface ProductionAgentSystemCapstoneState {
  sessionId: string
  stepCount: number
  stepBudget: number
  checkpoint: CheckpointId
  // project-specific fields elided
}

Architecture Template for Every AI Agent Project

Every project above follows the same structural template. The components change, but the layers do not:

Experience layer: How the user or system submits a goal and receives results.
Orchestration layer: The execution loop that assembles context, calls the model, executes tools, and manages checkpoints.
State layer: Checkpointed state with idempotency keys and a loop budget.
Tools layer: Scoped tools with schemas, permission boundaries, and audit logs.
Retrieval and memory layer: Per-session retrieval with grounding checks; optional long-term memory with retention policies.
Control plane: Tracing, evaluation, guardrails, approval gates, and cost monitoring.

A project that omits any of these layers has a gap. A project that implements all of them — even simply — is portfolio-worthy. For a detailed breakdown of each layer, see the Agentic AI architecture guide.

Read the full Agentic AI architecture guide

How to Evaluate an Agentic AI Project

Evaluation is what separates a project from a demo. Measure these dimensions on a real evaluation dataset — not on a handful of hand-picked examples:

Task completion: Did the agent achieve the goal? Measure on a dataset of 20+ tasks.
Trajectory quality: Were the steps efficient and correct? Inspect the full trace, not just the final answer.
Tool-call correctness: Did the agent call the right tool with the right arguments? Measure per-call accuracy.
State integrity: Did state remain consistent after retries and recovery? Test with injected failures.
Safety compliance: Did the agent stay within permission boundaries and approval gates? Audit every tool call.
Latency and cost: Measure end-to-end latency and cost per task. Track against a budget.

Final-answer quality alone is insufficient. An agent that produces a correct answer through a broken trajectory is not production-ready. For a detailed evaluation framework, see the AI agent evaluation guide.

Read the full AI agent evaluation guide

Failure Cases Your Demo Must Survive

A portfolio-worthy project demonstrates recovery from at least these failure scenarios. Each scenario should have a trace showing the failure and the recovery path.

Failure case 1

Tool timeout. A tool call hangs. The agent must time out, retry with backoff, and either succeed or fall back — not wait indefinitely.

Failure case 2

State corruption after retry. A retried step writes state twice. The agent must use idempotency keys and transactional state updates to prevent corruption.

Failure case 3

Approval denial. A human rejects a proposed action. The agent must accept the denial, update state, and continue or stop — not retry the action.

Failure case 4

Loop budget exceeded. The agent hits the max-step or max-cost limit. It must stop gracefully and report progress — not loop forever.

Failure case 5

Retrieval returns irrelevant results. The retrieval tool returns off-topic content. The agent must detect low relevance, rerank or re-retrieve, and ground its answer — not hallucinate.

What Evidence to Include in Your Portfolio

A project README is your primary evidence. Structure it so a reviewer can assess system ownership in under five minutes:

Project README evidence checklist

Problem statement: what real problem does this solve?
Why an agent is justified: what decisions require model reasoning?
Architecture diagram: layers, state store, tools, control plane
Tool permissions: schema, scope, and audit log for each tool
Evaluation results: metrics on a real dataset, not hand-picked examples
Trace example: a full trajectory showing state, tools, and decisions
Failure cases handled: at least three, with recovery demonstrations
Deployment approach: containerized, with a documented production plan
Retrospective: what you would do differently next time

Common Agent Project Mistakes

Overbuilding. Adding multi-agent, long-term memory, or complex retrieval before the single-agent loop is solid. Each layer should solve a real requirement — not demonstrate a technique.
No evaluation. Showing outputs without metrics. A project without evaluation is a demo, not evidence.
No failure handling. Assuming tools always succeed and state never corrupts. A project that cannot recover from failure is not production-ready.
No state persistence. Keeping state in memory only. An agent that loses all progress on restart is not portfolio-worthy.
Too many agents. Using multi-agent without justifying the coordination cost. A single agent with solid state, tools, and evaluation is stronger evidence than a multi-agent system that lacks control.
Over-privileged tools. Giving an agent broad database or shell access. Each tool should be a narrow, purpose-built operation with least-privilege credentials.

From Independent Build to Reviewed Capstone

Independent projects build skill. A reviewed capstone builds credibility. The capstone is where an experienced engineer assesses your architecture, code quality, tool behaviour, state handling, reliability, safety, and deployment readiness — and gives you feedback you cannot give yourself.

If you have built one or more of the projects above and want a structured review path, the Agentic AI Course includes guided engineering projects and a production capstone reviewed across all the dimensions this guide covers.

See what you will build in the Agentic AI Course

Agentic AI Projects — Frequently Asked Questions

Which Agentic AI project should a beginner build first?

Start with a tool-using research agent — one model, one retrieval tool, structured output. Prove state and grounding before adding complexity.

What makes an AI agent project different from a chatbot?

State, tools, evaluation, recovery. A chatbot generates text. An agent project manages state, calls tools within permissions, handles failures, and measures task success.

Should every portfolio project use multiple agents?

No. A single agent with solid state, tools, and evaluation is more impressive than a multi-agent system that lacks control. Use multi-agent only when justified.

How should I evaluate an AI agent project?

Task completion, trajectory quality, tool-call correctness, state integrity, safety compliance, latency, and cost. Final-answer quality alone is insufficient.

Which Agentic AI projects are useful for software engineers?

Tool-using agents, MCP-based enterprise tool agents, and browser agents — they leverage API integration, state management, and security skills you already have.

What should an Agentic AI project README contain?

Problem statement, why an agent is justified, architecture diagram, tool permissions, evaluation results, failure cases handled, deployment approach, and what you would do differently.

How do I demonstrate production readiness without a large cloud budget?

Local deployment with Docker, evaluation datasets, trace examples, and a documented deployment plan. Production readiness is about controls, not scale.

Related Agentic AI Guides

BUILD WITH REVIEW

Build a Portfolio That Shows System Ownership.

The Agentic AI Course includes six guided engineering projects and a production capstone reviewed for architecture, code quality, tool behaviour, state handling, reliability, safety and deployment readiness.

12 weeks

Live online

6 guided projects

+ production capstone

8–10 hours/week

Weekly commitment

₹35,000

One-time fee

See What You Will Build View Agentic AI Course Details

Reviewed by School of Core AI Technical Training Team

Sources and Methodology

This guide synthesizes engineering guidance from primary sources: Google Cloud agent architecture documentation, Microsoft AI agent guidance, LangGraph documentation, and the Model Context Protocol specification. Project descriptions are engineering references designed to demonstrate system ownership — not step-by-step tutorials. Framework and protocol capabilities were verified at the time of writing.

Published: 2026-09-08·Last reviewed: 2026-09-08·Review owner: School of Core AI Technical Training Team·Version: 1.0

Google Cloud — Architecture guidance for Agentic AIProduction agent architecture patterns and project structure guidance.
Microsoft — AI agent guidance and patternsSingle-agent vs multi-agent guidance; project and deployment patterns.
LangGraph — Official documentationStateful agent projects, checkpointing, and evaluation patterns.
Model Context Protocol SpecificationMCP for enterprise tool integration and trust boundaries in agent projects.

Framework capabilities change. Verify current documentation before implementation. This guide avoids permanent statements such as “Framework X is always best.”