AI Agents Masterclass — Full Visual Guide

Everything you need to understand, compare, and build AI agents: definitions from Google Cloud and IBM, ReAct and ReWOO loops, multi-agent patterns, 15+ frameworks, MCP and A2A protocols, governance, Cloud Run deployment, and five runnable examples with animated diagrams + terminal GIFs.

What you’ll understand at the end

• What an AI agent is — and how it differs from assistants, chatbots, and bots
• Six core capabilities: reasoning, acting, observing, planning, collaborating, self-refining

• Agent anatomy : persona, memory, tools, model
• Memory tiers  — working, episodic, semantic, procedural
• ReAct and ReWOO reasoning paradigms
• Five classical agent types on the reflex → learning ladder
• Three lifecycle stages: goal planning, tool reasoning, learning/reflection
• Single vs multi-agent , surface vs background deployment
• Agentic vs non-agentic chatbots
• Six enterprise use-case families with healthcare, finance, and emergency examples
• Benefits, challenges, and governance patterns (HITL, activity logs, interruption)
• 15+ frameworks  — when to pick LangGraph, CrewAI, OpenAI Agents SDK, Pydantic AI, Hermes, OpenClaw, and more
• MCP + A2A interoperability
• Cloud Run production deployment
• Five runnable examples with terminal GIFs and smoke tests

Introduction — why agents now

For years, “AI” in products meant one-shot generation : you send a prompt, the model returns text, the transaction ends. That works for drafting emails. It fails for real work — research a market, book travel, triage tickets, reconcile accounts — because real work is multi-step , tool-dependent , and stateful.

An AI agent closes that gap. Instead of a single response, the system pursues a goal over time: it plans, calls tools, reads results, revises, and stops when the objective is met (or when a human says stop).

Industry definitions converge on the same idea with different emphasis:

Google Cloud describes AI agents as systems that combine a foundation model with reasoning , planning , and action  — using tools and external data to accomplish tasks on a user’s behalf, not just answer questions.

IBM frames agents as software entities that perceive their environment, reason about goals, and act through tools or APIs — often with memory that persists across interactions.

OpenAI’s practical guide adds product reality: agents shine when workflows are open-ended , require judgment , and benefit from tool use  — but they demand stronger observability and guardrails than chatbots.

This masterclass synthesizes those views into one buildable mental model, then walks you through code, frameworks, and production patterns.

Part 1 — Agent vs assistant vs bot

Three labels get swapped in marketing. Architecturally, they differ:

Bot (classic) — rule-based or intent-classifier driven. Fixed dialog trees, slot filling, no genuine planning. Example: “Track my package” → lookup by tracking number. Predictable, cheap, brittle outside trained intents.

Assistant (LLM chatbot) — a model in a chat UI. Strong at language, weak at persistence. Each turn is mostly stateless unless you bolt on memory. Example: “Summarize this PDF” in one shot. No tool loop unless explicitly wired.

Agent  — an LLM (or ensemble) wrapped in a control loop : plan → act via tools → observe results → repeat. Carries goal state , memory , and often delegation to other agents. Example: “Find the best week for surfing in Greece next year” → weather DB → tide search → synthesize → recommend dates.

Agent vs assistant vs bot

Rule of thumb in prose: if the product only needs one model call and no side effects, use an assistant. If it must change the world (APIs, DBs, files, tickets) over multiple steps, you are building an agent. If the flow is fully scripted with no LLM judgment, you might not need an agent at all — a workflow engine suffices.

Part 2 — Six defining capabilities

Modern agents are not defined by a single feature but by a bundle of behaviors:

Reasoning  — the model decomposes goals, handles ambiguity, and chooses among strategies. Chain-of-thought and structured planning prompts live here.

Acting  — execution through tools : HTTP calls, SQL, Python, browser automation, MCP servers. Action is what separates agents from chat.

Observing  — after each action, the agent ingests tool output (JSON, logs, errors) and updates its internal state. Bad observation handling is the #1 source of silent failures.

Planning  — explicit or implicit task graphs: “first gather weather, then check tides, then compare weeks.” Plans may be static (ReWOO) or interleaved with execution (ReAct).

Collaborating  — multi-agent handoffs, human approvals, or role-based crews. No single model must do everything.

Self-refining  — reflection passes, critique steps, memory writes, skill authoring. The agent improves its approach within or across sessions (see Hermes learning loop).

Agent anatomy — persona, memory, tools, model

These six capabilities map directly to architecture choices later: tools need MCP or function schemas; collaboration needs handoff or crew abstractions; self-refining needs memory tiers and logging.

Part 3 — Anatomy: persona, memory, tools, model

Every production agent resolves into four layers:

Persona  — system prompt, SOUL.md, role brief. Sets tone, boundaries, and escalation rules. In enterprise agents, persona also encodes compliance (“never disclose account numbers”).

Memory  — what persists beyond the current context window. Short-term: chat history and scratchpad. Long-term: vector stores, markdown files, session DBs. See Part 4.

Tools  — typed functions the model can invoke. Each tool needs a name, description, JSON schema, and a handler. Tools should be narrow and idempotent where possible.

Model  — the reasoning engine. Often one primary model plus smaller models for routing or summarization. Model choice affects cost, latency, and tool-call reliability.

# Conceptual agent stack (not framework-specific)
agent = {
    "persona": "You are a cautious travel planner. Confirm before booking.",
    "memory": {"session": [], "long_term": "vector://user-prefs"},
    "tools": ["weather_db", "search_web", "calendar_create"],
    "model": "gpt-4o",
}

The model is interchangeable; tools and memory encode your product’s real value.

Part 4 — Memory tiers

Memory is not one blob. Mature agents use tiers with different latency, capacity, and retrieval patterns:

Working memory  — the current context window: system prompt, recent turns, tool results. Bounded by token limits; compress or summarize when full.

Episodic memory  — past sessions and events (“last time we planned Greece, user preferred July”). Stored in SQLite, Postgres, or session logs; retrieved by recency or search.

Semantic memory  — facts and embeddings in a vector store. “User is vegetarian.” “API X rate-limits at 100 rpm.”

Procedural memory  — skills, playbooks, SOUL-adjacent instructions. Often markdown files or skill catalogs (Hermes SKILL.md, OpenAI custom instructions at scale).

Memory tiers — working, episodic, semantic, procedural

Design rule: inject a small frozen snapshot at session start (persona + top facts), then let the agent search for deeper history on demand. Dumping entire history into every turn burns context and money.

Many enterprise agents rely on retrieval systems rather than storing all knowledge directly inside the model context window. Platforms such as Instant RAGFlow provide document ingestion, indexing, and retrieval pipelines that allow agents to access relevant information dynamically while keeping prompts lean and up to date.

Link: https://techlatest.net/support/ragflow_support/

Semantic memory is commonly implemented using vector databases that store embeddings and enable similarity search. Chroma Vector Database is a popular lightweight option for agent memory systems, helping agents retrieve relevant facts, previous interactions, and domain knowledge during execution.

Link: https://techlatest.net/support/chromadb_support/

Part 5 — ReAct: interleaved reasoning and action

ReAct (Reason + Act) alternates thought , tool call , and observation in one loop. The model decides the next step only after seeing the last observation.

Typical trace:

1. Think: “I need historical weather for Greece.”
2. Act: weather_db("Greece")
3. Observe: { "avg_sunny_days_july": 28 }
4. Think: “Need tide/surf conditions.”
5. Act: search_web("best surfing tide Greece")
6. Observe: snippet about high tide windows
7. Think: “Combine signals → recommend July 12–19.”
8. Act: respond to user

ReAct loop — think, act, observe

ReAct is flexible — the plan emerges from execution. That helps exploratory tasks. Cost: more model turns, harder to audit upfront.

Our minimal example implements this pattern (deterministic demo without an API key):

# examples/minimal_react_agent.py (excerpt)
def think_and_act(state: AgentState, turn: int) -> None:
    if turn == 0:
        state.steps.append("Think: need historical weather for Greece")
        out = TOOLS["weather_db"]("Greece")
        state.steps.append(f"Act: weather_db → {out}")
    elif turn == 1:
        state.steps.append("Think: need surfing conditions (high tide)")
        out = TOOLS["search_web"]("best surfing tide Greece")
        state.steps.append(f"Act: search_web → {out}")
    elif turn == 2:
        state.steps.append("Observe: combine tide + sunny patterns")
        state.steps.append("Act: recommend week of July 12–19 (demo)")
        state.done = True

Run:

cd guides/ai-agents-masterclass
python examples/minimal_react_agent.py

Part 6 — ReWOO: plan first, execute second

ReWOO (Reasoning Without Observation in the loop) separates planning from execution. A planner emits a structured script of tool calls; a worker runs them; a solver synthesizes the final answer.

Flow:

1. Planner  — output tool call graph with placeholders
2. Worker  — execute all tools (possibly in parallel)
3. Solver  — read outputs, no further tool access

ReWOO flow — planner, worker, solver

When ReWOO wins: predictable pipelines, expensive tools, parallelizable subtasks, audit requirements (plan is reviewable before execution).

When ReAct wins: ambiguous goals, errors mid-flight, need to branch on unexpected results.

Many production systems hybridize : ReWOO for the macro pipeline, ReAct inside a single step when debugging.

Part 7 — Five classical agent types

Before LLMs, agent literature defined a ladder of sophistication. Still useful for scoping:

Simple reflex  — if condition then action. Thermostat, basic alert bot. No memory, no search.

Model-based reflex  — internal state tracks the world (last sensor reading). Still no planning.

Goal-based  — searches action sequences to reach a goal. Classical planning / STRIPS territory.

Utility-based  — optimizes tradeoffs (cost vs speed vs risk). Portfolio agents, routing.

Learning  — updates policy from feedback. RL agents, self-refining skill loops, GEPA-style offline evolution.

Agent types ladder — reflex to learning

LLM agents usually sit at goal-based with hooks toward learning (memory writes, reflection, fine-tuning). Don’t over-build learning before basic tool reliability works.

Part 8 — Three lifecycle stages (surfing vacation)

OpenAI and ServiceNow-style masterclasses often teach agents as three stages. We use one running example: “Best week for surfing in Greece next year.”

Stage 1 — Goal planning

Decompose the user goal into subtasks and success criteria.

• Subtask A: historical weather / sunny weeks
• Subtask B: surf/tide suitability
• Subtask C: reconcile constraints (user budget, travel dates)
• Done when: ranked recommendation with confidence

Goal planning — decompose and prioritize

User goal: "Best week for surfing in Greece next year"
Planner output:
  1. Query weather_db(Greece) for sunny weeks
  2. search_web for tide/surf windows
  3. Rank weeks; explain tradeoffs

Stage 2 — Tool reasoning

Select tools, fill arguments, handle errors, retry with backoff. The model must not invent tool names — bind to your schema.

Tool reasoning — schema-bound calls

TOOLS = {
    "search_web": search_web,
    "weather_db": weather_db,
}
# Model sees JSON schemas; handler validates before side effects

Stage 3 — Learning and reflection

After answering, optionally: log trace, write memory (“user cares about surfing”), critique weak steps, update skills. This is where agents compound over time.

Learning loop — trace to memory to skills

Reflection: "weather_db lacked tide granularity — add surf_forecast tool next sprint"
Memory write: USER prefers July travel

Agent lifecycle — plan, act, learn

Part 9 — Agentic vs non-agentic chatbots

Non-agentic chatbot  — single-turn or few-turn Q&A. Retrieval augments context, but no autonomous tool loop. Great for FAQs, doc search, copilot suggestions.

Agentic chatbot  — same UI, but backend runs a control loop with tools and state. User may see “Searching…” / “Calling calendar…” steps.

Differences that matter in production:

• Latency  — agents take longer; set UX expectations
• Cost  — multiple model + tool calls per user message
• Failure modes  — tool errors, infinite loops, hallucinated arguments
• Observability  — you need step traces, not just final text

If your feature is “answer from our PDF,” start non-agentic. If it is “file this ticket and follow up,” go agentic.

Part 10 — Single vs multi-agent

Single agent  — one model, one loop, one tool namespace. Simplest to debug. Hits limits on long workflows and conflicting roles.

Multi-agent  — specialized agents with handoffs or parallel crews. Examples: triage → specialist, researcher + writer, planner + executor.

Single vs multi-agent topologies

Patterns in prose:

• Sequential crew  — A completes task, passes output to B (CrewAI default)
• Handoff  — router agent transfers conversation to specialist (OpenAI Agents SDK)
• Supervisor  — orchestrator assigns subtasks to workers (LangGraph, AutoGen)
• Debate/review  — generator + critic for quality gates

Multi-agent adds coordination overhead. Start single-agent until you have clear role boundaries and separate tool permissions per role.

Part 11 — Surface vs background agents

Surface agents  — user-facing, synchronous. Chat UI, voice, copilot pane. User waits for steps; HITL approvals live here.

Background agents  — async jobs: cron digests, ticket sweeps, ETL monitors. Results delivered later via email, Slack, or dashboard.

Surface vs background deployment

Hermes cron and OpenClaw heartbeats are background patterns. Cloud Run jobs or scheduled Cloud Functions fit the same slot.

Design background agents with idempotency and dead-letter queues  — they will retry at 3 am without a human watching.

Part 12 — Six use-case categories

Enterprise agents cluster into six families (plus cross-industry patterns):

Six use-case categories

1. Customer experience  — support triage, order status, personalized recommendations. Needs CRM tools, strict PII handling.

2. Employee productivity  — draft docs, schedule meetings, summarize threads. Microsoft 365 Copilot, Google Workspace agents.

3. Software development  — issue → PR agents, test generation, migration assistants. Heavy IDE + repo tool access.

4. Data and analytics  — natural language to SQL, anomaly explanation, report generation. Guard against destructive queries.

5. Security and operations  — alert triage, runbook execution, patch verification. Read-only first; HITL for mutations.

6. Industry workflows  — vertical bundles (see below).

Healthcare

Clinical documentation agents draft notes from visit audio — human sign-off required. Prior authorization agents gather payer rules and patient history. Scheduling agents coordinate slots across systems. Regulatory constraint: agents assist ; they do not diagnose autonomously in regulated jurisdictions.

Finance

Reconciliation agents match transactions across ledgers. Research agents summarize filings and earnings calls with citations. Compliance agents flag policy violations in communications. Audit trails and model risk management are mandatory.

Emergency and public safety

Dispatch assist agents summarize 911 transcripts and suggest resource allocation — always subordinate to human dispatchers. Disaster response agents aggregate feeds and produce situational reports. Latency and failure modes can be life-critical; degrade gracefully to static playbooks.

Part 13 — Benefits

Automation of judgment-heavy workflows  — not just repetitive clicks, but branching decisions with explanations.

24/7 operation  — background agents monitor queues overnight.

Composable tools  — same agent core, swap MCP servers for new domains.

Personalization at scale  — memory tiers remember preferences without re-prompting.

Faster iteration  — natural language interfaces to internal APIs lower integration cost.

Part 14 — Challenges and risks

Unpredictability  — same prompt, different tool paths. Mitigate with schemas, evals, and golden traces.

Cost  — long ReAct loops multiply token usage. Cap turns, summarize observations.

Security  — prompt injection via tool results, over-privileged tools, SSRF from web fetch tools. Least privilege per tool.

Compliance  — GDPR, HIPAA, SOC2: log retention, data residency, human approval for sensitive actions.

Trust  — users need visibility into what the agent did. Black-box answers erode adoption.

Governance — HITL, logs, policies

Part 15 — Best practices

Activity logs  — append-only trace of every thought, tool call, observation, and final output. Store run_id, timestamps, user ID, model version.

Interruption  — user can cancel in-flight loops; worker checks cancel token between turns (Hermes models this explicitly).

Unique IDs  — correlate user session, agent run, and tool invocations across microservices.

Human-in-the-loop (HITL) — require approval for payments, deletes, external emails, privilege changes. Pattern: agent prepares action → human clicks approve → tool executes.

Tool design  — small surface area, explicit errors, no silent defaults on missing args.

Evals  — regression suite of goals with expected tool sequences or output rubrics.

Budgets  — max turns, max tool calls, max cost per run.

# Pseudocode: run envelope
@dataclass
class RunContext:
    run_id: str
    user_id: str
    max_turns: int = 12
    cancelled: bool = False

def step(ctx: RunContext):
    if ctx.cancelled:
        raise RunCancelled(ctx.run_id)
    log_event(ctx.run_id, "tool_call", {...})

Part 16 — Protocols: MCP and A2A

Agents rarely exist alone. Two interoperability layers matter in 2025–2026:

Model Context Protocol (MCP)

MCP standardizes how hosts discover and invoke tools, resources, and prompts from external servers — “USB-C for AI tools.” Your agent (or IDE host) runs MCP clients; GitHub, Postgres, filesystem, custom APIs expose MCP servers.

Deep dive: MCP Visual Guide.

Protocols — MCP and A2A

Agent-to-Agent (A2A)

A2A (Google-led, industry collaborators) focuses on agent ↔ agent messaging: capability cards, task delegation, status updates across vendor boundaries. Where MCP connects agents to tools , A2A connects agents to each other.

Use MCP for tool sprawl; use A2A when your orchestrator and specialist run in different frameworks or clouds and need a standard task envelope.

Part 17 — Framework landscape

No single framework wins every workload. Map orchestration style , team familiarity , and deployment target first.

Frameworks map — LangGraph, CrewAI, SDKs, cloud

Below: when to use prose for each major option. All can coexist with MCP tool servers.

LangGraph

LangGraph models agents as state machines  — nodes, edges, conditional routing, checkpointing. Best when you need explicit control flow , cycles, human-in-the-loop interrupts, and time-travel debugging. LangChain ecosystem; steep learning curve if you only need a simple ReAct loop.

# examples/langgraph_research_agent.py — plan → research → synthesize
g = StateGraph(ResearchState)
g.add_node("plan", plan)
g.add_node("research", research)
g.add_node("synthesize", synthesize)
g.set_entry_point("plan")
g.add_edge("plan", "research")
g.add_edge("research", "synthesize")
g.add_edge("synthesize", END)
app = g.compile()

Pick LangGraph for production workflows with branching, retries, and persisted state.

CrewAI

CrewAI optimizes role-based teams : researcher, writer, analyst with sequential or hierarchical process. Minimal boilerplate for multi-agent prose tasks. Less ideal for fine-grained tool graphs or hard latency SLAs.

crew = Crew(
    agents=[researcher, writer],
    tasks=[research_task, write_task],
    process=Process.sequential,
)
result = crew.kickoff()

Pick CrewAI for content pipelines , research briefs, and demos where roles are obvious.

AutoGen (Microsoft)

AutoGen emphasizes conversable agents and group chat patterns — good for coding assistants, multi-agent debate, and Azure/OpenAI shops. v0.4+ rearchitecture adds async and distributed agents. Choose when you want Microsoft stack integration and flexible agent-to-agent chat.

OpenAI Agents SDK

OpenAI Agents SDK (openai-agents) provides Agent , Runner , handoffs , and built-in tracing. Tight integration with OpenAI models and Responses API. Handoffs are first-class for triage → specialist routing.

specialist = Agent(name="Specialist", instructions="Answer technical AI agent questions.")
triage = Agent(name="Triage", instructions="Route technical questions.", handoffs=[specialist])
result = await Runner.run(triage, "What is ReAct for AI agents?")

Pick it for OpenAI-native products and fast handoff prototypes.

Google Agent Development Kit (ADK)

Google ADK targets Gemini agents on Vertex AI and Google Cloud — tool use, sub-agents, deployment to Cloud Run. Choose when your stack is GCP-first, and you want first-party Google tooling for evals and hosting.

Pydantic AI

Pydantic AI centers type-safe outputs  — result_type=WeatherReport Validates structured responses. Excellent developer ergonomics for Python teams already using Pydantic v2.

class WeatherReport(BaseModel):
    location: str
    best_week: str
    confidence: float
    notes: str

agent = Agent("openai:gpt-4o-mini", result_type=WeatherReport, system_prompt="...")
result = agent.run_sync("Best surfing week in Greece?")

Pick Pydantic AI when schema correctness matters more than exotic orchestration.

LlamaIndex Agents

LlamaIndex began as RAG; its agent layer excels when retrieval is the core — document Q&A agents, knowledge-base tools, hybrid search. Pair with LlamaParse and workflow events for ingestion-heavy apps.

Semantic Kernel (Microsoft)

Semantic Kernel offers plugins, planners, and enterprise patterns in .NET and Python. Strong fit for Microsoft 365 , Azure AI, and orgs with existing SK investments.

Smolagents (Hugging Face)

Smolagents  — lightweight, code-agent focused, Hugging Face hub models. Great for local/open models and teaching agents without heavy deps.

Amazon Bedrock Agents

Bedrock Agents  — managed AWS service: action groups, knowledge bases, guardrails. Choose when you want AWS-managed scaling and IAM-native permissions, less custom loop code.

Mastra

Mastra  — TypeScript-first agent framework with workflows, evals, and deployment story. Pick for Node/TS teams building product agents alongside Next.js apps.

Agno (formerly Phidata)

Agno  — Python toolkit for multi-agent systems with memory, knowledge, and UI. Fast prototyping for agent OS style apps.

ServiceNow AI Agents

ServiceNow embeds agents in ITSM, HR, CSM workflows — Now Assist, flow designer integration, enterprise guardrails. Choose when the workflow already lives in ServiceNow; extend via Now Platform skills and data classes.

Hermes Agent

Hermes  — self-hosted learning agent : SOUL.md identity, three memory tiers, self-evolving skills, Curator, optional GEPA, MCP-heavy profiles, gateway + cron. Best when you want an agent that improves over time on your machine.

Full tutorial: Hermes Agent Masterclass.

OpenClaw

OpenClaw  — messaging-first gateway (WhatsApp, Telegram, Slack), ClawHub skills, proactive heartbeats. Best when channels and presence matter more than offline skill evolution. Compare: Hermes vs OpenClaw.

Framework selection (prose)

• Explicit graphs, HITL, persistence → LangGraph
• Role crews, content → CrewAI
• OpenAI handoffs → OpenAI Agents SDK
• Typed Python outputs → Pydantic AI
• RAG-heavy → LlamaIndex
• GCP / Gemini → Google ADK
• AWS managed → Bedrock Agents
• TypeScript product → Mastra
• Self-hosted learning agent → Hermes
• Messaging gateway → OpenClaw

Organizations deploying customer-facing agents often need more than orchestration alone. OpenClaw provides a messaging-first architecture with support for channels such as WhatsApp, Telegram, and Slack, enabling agents to operate continuously across real-world communication platforms while maintaining isolated sessions and tool access controls.

Link: https://techlatest.net/support/openclaw-support/

Part 18 — Environment setup

Prerequisites:

• Python 3.11+
• Optional: OPENAI_API_KEY for live LLM runs
• Virtualenv recommended

cd guides/ai-agents-masterclass
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt # optional deps per framework
cp .env.example .env # fill OPENAI_API_KEY if desired

Part 19 — Example 1: minimal ReAct agent

File: minimal_react_agent.py

No framework — pure Python demonstrating Think → Act → Observe. Uses stub weather_db and search_web tools. Set AGENT_GOAL in .env.

python examples/minimal_react_agent.py

Expected output: step trace ending in ✓ ReAct loop completed.

Step 02 — minimal ReAct run

Teaching point: understand the loop before adopting LangGraph or CrewAI abstractions.

Part 20 — Example 2: LangGraph research agent

File: langgraph_research_agent.py

Three-node graph: plan → research → synthesize. Writes report.md.

pip install langgraph langchain-core
export RESEARCH_TOPIC="AI agent governance"
python examples/langgraph_research_agent.py

Step 03 — LangGraph research agent

Extend with conditional edges: if research finds insufficient sources, loop back to research.

Part 21 — Example 3: CrewAI content crew

File: crewai_content_crew.py

Two agents — researcher and writer — sequential tasks. Demo mode writes stub blog_draft.md without API key.

pip install crewai
export CREW_TOPIC="Why AI agents need governance"
python examples/crewai_content_crew.py

With OPENAI_API_KEY, runs live crew and saves markdown output.

Step 04 — CrewAI content crew

Part 22 — Example 4: OpenAI Agents SDK handoffs

File: openai_agents_sdk.py

Async triage → specialist handoff via openai-agents.

pip install openai-agents
export OPENAI_API_KEY=sk-...
python examples/openai_agents_sdk.py

Step 05 — OpenAI Agents SDK handoff

Tracing in OpenAI dashboard shows handoff boundaries — use for debugging routing.

Part 23 — Example 5: Pydantic AI typed agent

File: pydantic_ai_typed_agent.py

Returns validated WeatherReport model — location, best_week, confidence, notes.

pip install pydantic-ai
python examples/pydantic_ai_typed_agent.py # demo stub without key
export OPENAI_API_KEY=sk-...
python examples/pydantic_ai_typed_agent.py # live validated run

Step 06 — Pydantic AI typed output

Use typed agents at API boundaries — downstream code consumes Pydantic models, not raw strings.

Part 24 — Smoke tests

Run the bundled pytest smoke tests (no API key required for stubs):

pip install pytest
pytest examples/tests/test_agents_smoke.py -v

Step 07 — run tests

Part 25 — Deploy to Google Cloud Run

Containerize your agent HTTP service or job runner. Cloud Run gives scale-to-zero, IAM, and VPC connectors for private DB access.

Outline:

1. Dockerfile  — slim Python image, install deps, expose port 8080
2. Service  — FastAPI or Flask wrapper around agent run() with run_id logging
3. Secrets  — Secret Manager for OPENAI_API_KEY, not env files in image
4. Deploy  — gcloud run deploy agent-service --source .
5. Background  — Cloud Run jobs or Cloud Scheduler for cron agents

Cloud Run deployment — container to service

# Minimal Dockerfile sketch
FROM python:3.12-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY . .
ENV PORT=8080
CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8080"]

gcloud run deploy ai-agent-demo \
  --source . \
  --region us-central1 \
  --set-secrets OPENAI_API_KEY=openai-key:latest \
  --allow-unauthenticated # lock down in production

Production notes:

• Set request timeout above worst-case agent duration or return 202 + poll
• Use Cloud Logging for structured trace JSON
• Attach service account with least privilege for GCP tools
• Consider Cloud Armor if endpoint is public

Part 26 — Production checklist

Before shipping any agent to users:

Identity and auth  — who can invoke which tools? Map OAuth subject → tool ACL.

Observability  — structured logs, metrics (turns, latency, tool errors), distributed tracing.

Safety  — input/output filters, blocked tool list, prompt injection tests on tool results.

HITL  — approval queue for irreversible actions.

Cost controls  — per-user budgets, model routing (small model for triage).

Data  — PII redaction in logs, retention policy, regional storage.

Reliability  — idempotent tools, retries with jitter, circuit breakers on flaky APIs.

Evals  — golden tasks in CI; regression when prompts or tools change.

Incident response  — kill switch to disable tool execution globally.

Documentation  — runbooks for on-call when agent error rate spikes.

Moving from prototypes to production often requires workflow management, monitoring, and operational controls around agent systems. Dify AI provides a platform for building, deploying, evaluating, and monitoring AI agents and LLM applications, helping teams shorten the path from experimentation to production deployment.

Link: https://techlatest.net/support/difyai_support/

Part 27 — Building your own agent (checklist)

1. Define one measurable goal (surf week, ticket triage, report generation)
2. List tools with JSON schemas — prefer MCP servers for reuse
3. Choose ReAct vs ReWOO (or hybrid)
4. Pick framework from Part 17 or start with minimal loop
5. Add memory tier only when sessions need continuity
6. Instrument run_id and step logs from day one
7. Ship HITL before auto-executing side effects
8. Run smoke tests and golden evals
9. Deploy behind API with timeouts and secrets manager
10. Iterate from traces — most bugs are bad tool descriptions, not bad models

Part 28 — Connecting agents to MCP

Any framework above can call MCP tools if the host exposes them (Cursor, Claude Desktop) or you embed an MCP client in your runtime.

Pattern:

1. Run MCP server (stdio or HTTP)
2. Client handshake → discover tools
3. Map MCP tool schemas to your framework’s function format
4. Execute tool calls through MCP client

Cross-reference: MCP Visual Guide — Part 10–12.

Hermes profiles declare MCP in config.yamlLangGraph nodes can wrap MCP invocations in a dedicated tool node.

Part 29 — Multi-agent orchestration patterns

Supervisor  — central node assigns subtasks, collects results. LangGraph Send API, AutoGen group chat.

Pipeline  — fixed DAG, no dynamic routing. CrewAI sequential, ReWOO workers.

Handoff  — conversational transfer with context pack. OpenAI Agents SDK.

Blackboard  — shared state document agents read/write. Useful for research synthesis.

Pick supervisor when tasks are dynamic; pipeline when steps are known; handoff when user-facing role should change mid-session.

As multi-agent systems grow in complexity, visual orchestration becomes increasingly valuable. CrewAI Studio allows developers to design, coordinate, and monitor role-based agent teams without building orchestration infrastructure from scratch, making it a practical choice for research, content generation, and business workflow automation.

Link: https://techlatest.net/support/crewai-support/

Part 30 — Observability and debugging

Trace format (store as JSON lines):

{
  "run_id": "run_abc123",
  "turn": 3,
  "type": "tool_call",
  "tool": "weather_db",
  "args": {"location": "Greece"},
  "latency_ms": 142,
  "status": "ok"
}

Debug workflow:

1. Reproduce with frozen prompt + tool stubs
2. Diff tool schemas vs model-emitted args
3. Check observation truncation — did you cut off the JSON the model needed?
4. Lower temperature for routing; allow higher for creative synthesis steps

OpenAI Agents SDK and LangSmith offer hosted tracing; self-host with OpenTelemetry if required.

Part 31 — Cost and latency optimization

• Route trivial questions to a small model without tools
• Cache tool results (weather, FX rates) with TTL
• Parallelize independent tool calls (ReWOO worker stage)
• Summarize long observations before next turn
• Cap max turns and fail gracefully with partial answer
• Batch background agents off peak

Part 32 — Security deep dive

Tool privilege  — separate read and write tools; never give shell and send_email to the same agent without HITL.

Prompt injection via tools  — malicious webpage content instructs “ignore prior instructions.” Sanitize and summarize untrusted tool output.

SSRF  — fetch_url tools must block metadata IPs and internal ranges.

Secrets  — tools receive credentials from env/Secret Manager, not from model context.

Output  — prevent agents from leaking system prompts or other users’ data in multi-tenant setups.

Part 33 — Evals and quality gates

Build a golden set of 20–50 tasks:

# evals/surf_goal.yaml
goal: "Best week for surfing in Greece next year"
expect_tools: ["weather_db", "search_web"]
rubric: "Must cite weather and tide reasoning; confidence stated"

Run in CI on prompt/tool changes. Track pass rate over time. Add adversarial cases (missing tool, API 500, empty search results).

Part 34 — When not to build an agent

Skip agents when:

• Workflow is fully deterministic  — use Zapier, Temporal, Airflow
• Zero side effects  — RAG chatbot suffices
• Hard real-time  — sub-100ms SLAs don’t fit LLM loops
• Regulatory prohibition on autonomous action — keep human-only execution

Agents are a tool , not a mandate.

Part 35 — Roadmap: from demo to product

Week 1  — minimal ReAct + one real tool + logs

Week 2  — MCP server for tool isolation + HITL on writes

Week 3  — LangGraph or SDK with checkpointing + eval suite

Week 4  — Cloud Run deploy + secrets + monitoring dashboards

Ongoing  — memory tier, multi-agent only when traces prove bottleneck

Summary

An AI agent pursues goals through a loop of reasoning, tool action, and observation — not a single chat completion. Persona, memory, tools, and model form the anatomy; ReAct and ReWOO offer two orchestration strategies; single vs multi-agent and surface vs background deployments match different products. Enterprise value spans six use-case families; governance (logs, HITL, unique run IDs) separates demos from production. Use MCP for tools and A2A for cross-agent tasks. Start with minimal_react_agent.py, graduate to LangGraph , CrewAI , OpenAI Agents SDK , or Pydantic AI as requirements sharpen, deploy on Cloud Run with secrets and evals, and extend with Hermes or MCP when you need learning loops or standardized tool wiring.

Thank you so much for reading

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