How to Build an AI Agent System from Scratch

Most AI agent tutorials stop at "call an LLM in a loop." That produces a demo, not a system. The difference between the two is everything that happens after the LLM generates a response: verification, memory, error handling, tool orchestration, and the feedback loops that make the system improve over time.

This guide walks through building a production-grade AI agent system from the ground up -- six steps, each building on the last, each taking you further from "toy project" toward something you can actually trust with real work.

If you are new to the concept, start with our foundational guide: What Are AI Agents? For a survey of the platforms and frameworks already in this space, see AI Agent Systems.

What You Are Building

An AI agent system is a software architecture that coordinates one or more AI agents to accomplish complex goals through autonomous perception, reasoning, and action -- with built-in mechanisms for memory, tool use, and quality verification.

By the end of this tutorial, you will have a system with six capabilities:

1. LLM integration -- A model that can reason about tasks
2. Agent loop -- The perception-reasoning-action cycle that drives autonomy
3. Tool use -- The ability to interact with external systems via MCP
4. Memory -- Persistent knowledge that compounds across sessions
5. Quality gates -- Automated verification before output reaches a user
6. Deployment -- A system that runs reliably in production

Each section includes conceptual Python code. This is not a framework you copy-paste and run. It is an architecture you understand and adapt. The goal is comprehension, not cargo-culting.

Step 1: Choose Your LLM

The language model is the reasoning engine at the center of your agent system. Every other component -- tools, memory, quality gates -- feeds information to the model or acts on its outputs. Choosing the right model (or models) is the most consequential architectural decision you will make.

What Matters for Agent Work

Agent workloads are different from chat workloads. A chatbot needs to be conversational. An agent needs to be reliable. The qualities that matter:

• Tool calling accuracy -- Can the model reliably generate structured function calls with correct parameters? This is non-negotiable. An agent that mis-calls tools is worse than useless.
• Instruction following -- Can it adhere to system prompts, output schemas, and multi-step instructions without drifting? Agents live and die by instruction fidelity.
• Context window -- How much information can the model hold at once? Agent tasks often require long context: codebases, documentation, conversation history, tool outputs.
• Reasoning depth -- Can it decompose complex goals into subtasks, handle edge cases, and recover from unexpected tool outputs?

The Case for Model Routing

A single model is a bottleneck. Simple tasks (formatting, classification, syntax checks) do not need your most capable model. Complex tasks (architecture decisions, root cause analysis, multi-file code changes) do not tolerate your cheapest one.

The solution is model routing -- a tier system that matches task complexity to model capability:

MODEL_TIERS = {
    "fast": "claude-haiku",      # Simple tasks: formatting, classification
    "balanced": "claude-sonnet",  # Standard tasks: code generation, analysis
    "powerful": "claude-opus",    # Complex tasks: architecture, debugging
}

def route_model(task_complexity: str) -> str:
    """Route tasks to the appropriate model tier."""
    return MODEL_TIERS.get(task_complexity, MODEL_TIERS["balanced"])

Nevo uses exactly this pattern, routing across three Anthropic tiers. The result: you are not paying Opus prices for lint checks, and you are not trusting Haiku with architectural decisions. Cost drops. Quality stays high.

Practical Recommendation

Start with a single high-quality model (Claude Sonnet is a strong default for agent work in 2026). Get the rest of the architecture working. Add model routing later as an optimization. Premature routing adds complexity before you have the metrics to route intelligently.

Step 2: Set Up the Agent Loop

The agent loop is the heartbeat of your system. It is the cycle that makes an AI agent autonomous rather than reactive: perceive the current state, reason about what to do next, act, observe the result, and repeat until the goal is achieved or the system decides to stop.

The Core Loop

Every agent system, from the simplest to the most sophisticated, implements some version of this cycle:

class AgentLoop:
    def __init__(self, model, tools, max_iterations=25):
        self.model = model
        self.tools = tools
        self.max_iterations = max_iterations

    def run(self, goal: str) -> str:
        messages = [
            {"role": "system", "content": self.system_prompt()},
            {"role": "user", "content": goal},
        ]

        for iteration in range(self.max_iterations):
            response = self.model.generate(messages)

            if response.is_final_answer():
                return response.content

            if response.has_tool_calls():
                for tool_call in response.tool_calls:
                    result = self.tools.execute(tool_call)
                    messages.append({"role": "tool", "content": result})

            messages.append({"role": "assistant", "content": response.content})

        return "Max iterations reached without completing the goal."

Three elements make this loop work in practice:

Termination Conditions

An agent that cannot stop is an agent that burns your budget and your patience. Define explicit termination conditions:

• Goal achieved -- The model signals completion with a structured response
• Max iterations -- A hard ceiling prevents runaway execution
• Error threshold -- Three consecutive failures trigger escalation, not retry
• Token budget -- Cap the total tokens consumed per task

Without these, a confused agent will loop indefinitely, generating increasingly incoherent tool calls while your API bill climbs. Every production agent system needs circuit breakers.

System Prompt Engineering

The system prompt is not a suggestion. It is the operating manual your agent follows on every iteration. A weak system prompt produces a weak agent, regardless of model quality.

An effective agent system prompt includes:

• Identity and role -- What the agent is, what it specializes in
• Available tools -- What it can do and when each tool is appropriate
• Output format -- Exactly how to structure responses and tool calls
• Constraints -- What not to do, how to handle ambiguity, when to ask for clarification
• Escalation rules -- When to stop trying and report a problem

Structured Output

Raw text from an LLM is unreliable for downstream processing. Force structured outputs wherever possible:

TOOL_RESPONSE_SCHEMA = {
    "type": "object",
    "properties": {
        "action": {"type": "string", "enum": ["call_tool", "respond", "escalate"]},
        "tool_name": {"type": "string"},
        "tool_args": {"type": "object"},
        "reasoning": {"type": "string"},
    },
    "required": ["action", "reasoning"],
}

When the model outputs structured JSON for every decision, you can parse it deterministically, log it cleanly, and replay it for debugging. Unstructured output forces you to write brittle regex parsers that break whenever the model rephrase things slightly.

Step 3: Add Tool Use with MCP

An agent without tools is an agent with opinions. It can reason about what should happen, but it cannot make anything happen. Tool use is what separates an agent from a chatbot.

The Model Context Protocol (MCP) is the standard for connecting AI agents to external tools, data sources, and services. If you have not encountered it yet, see our deep dive: What Is MCP?

Why MCP Over Custom Integrations

Before MCP, every tool integration was bespoke. Want your agent to read files? Write a file-reading function. Want it to query a database? Write a database wrapper. Want it to call an API? Another wrapper. Each integration was tightly coupled to your specific agent loop, untestable in isolation, and unmaintainable at scale.

MCP replaces this with a universal client-server interface. You write a tool once as an MCP server, and any MCP-compatible agent can use it. The protocol handles capability discovery, argument validation, error reporting, and transport -- whether the server runs as a local subprocess or a remote HTTP service.

Implementing an MCP Client

Your agent system needs an MCP client that discovers available tools and executes them on the agent's behalf:

import json
import subprocess

class MCPClient:
    """Minimal MCP client using stdio transport."""

    def __init__(self, server_command: list[str]):
        self.process = subprocess.Popen(
            server_command,
            stdin=subprocess.PIPE,
            stdout=subprocess.PIPE,
            stderr=subprocess.PIPE,
        )
        self.tools = self._discover_tools()

    def _discover_tools(self) -> list[dict]:
        """Ask the server what tools it provides."""
        request = {
            "jsonrpc": "2.0",
            "method": "tools/list",
            "id": 1,
        }
        return self._send(request)["result"]["tools"]

    def execute(self, tool_name: str, arguments: dict) -> str:
        """Execute a tool and return the result."""
        request = {
            "jsonrpc": "2.0",
            "method": "tools/call",
            "params": {"name": tool_name, "arguments": arguments},
            "id": 2,
        }
        result = self._send(request)
        if "error" in result:
            raise ToolExecutionError(result["error"]["message"])
        return result["result"]["content"]

    def _send(self, request: dict) -> dict:
        """Send a JSON-RPC request and read the response."""
        self.process.stdin.write(json.dumps(request).encode() + b"\n")
        self.process.stdin.flush()
        line = self.process.stdout.readline()
        return json.loads(line)

Building Your First MCP Server

An MCP server exposes tools through a standardized interface. Here is a minimal example -- a server that provides file system operations:

from mcp.server import Server
from mcp.types import Tool, TextContent

server = Server("filesystem")

@server.tool()
async def read_file(path: str) -> str:
    """Read the contents of a file at the given path."""
    with open(path, "r") as f:
        return f.read()

@server.tool()
async def write_file(path: str, content: str) -> str:
    """Write content to a file at the given path."""
    with open(path, "w") as f:
        f.write(content)
    return f"Written {len(content)} bytes to {path}"

@server.tool()
async def list_directory(path: str) -> str:
    """List files and directories at the given path."""
    import os
    entries = os.listdir(path)
    return "\n".join(entries)

For a complete guide on building MCP servers, see How to Build Your Own MCP Server.

Essential Tool Categories

A production agent system needs tools in several categories. You do not need all of these on day one, but you should know the territory:

• File system -- Read, write, search, and navigate files
• Code execution -- Run scripts, compile code, execute tests
• Web access -- Fetch URLs, search the web, interact with APIs
• Database -- Query, insert, update, delete data
• Communication -- Send messages, notifications, emails
• Observability -- Logs, metrics, traces, system state

The power of MCP is that each of these is a separate server. Your agent discovers them at startup, and the protocol handles the rest. Adding a new capability means spinning up a new MCP server, not rewriting your agent loop.

Step 4: Add Memory

Without memory, every session starts from zero. Your agent forgets the user's preferences, the project's architecture, the lessons from last week's debugging session, and the rules it derived from its own mistakes. Memory is what separates an agent that compounds in value from one that plateaus on day one.

Types of Memory

Agent memory operates at multiple timescales:

Working memory is the conversation context -- the messages, tool outputs, and reasoning accumulated during the current task. It lives in the LLM's context window and disappears when the session ends. Every agent has this by default.

Short-term memory persists across tasks within a session. It tracks what the agent has already done, what files it has read, what decisions it has made. This prevents the agent from re-reading the same file five times or making contradictory decisions within a single work session.

Long-term memory persists across sessions. It stores user preferences, project knowledge, derived rules, learned procedures, and accumulated expertise. This is where the compound effect lives. Building long-term memory is the hardest problem in agent architecture, and the most rewarding to solve.

Implementing a Memory System

A practical memory system combines structured storage with semantic search:

import json
import time
from pathlib import Path

class AgentMemory:
    def __init__(self, memory_dir: str):
        self.memory_dir = Path(memory_dir)
        self.memory_dir.mkdir(parents=True, exist_ok=True)
        self.facts_file = self.memory_dir / "facts.jsonl"
        self.rules_file = self.memory_dir / "rules.jsonl"

    def store_fact(self, fact: str, source: str, importance: float = 0.5):
        """Store a fact learned during operation."""
        entry = {
            "type": "fact",
            "content": fact,
            "source": source,
            "importance": importance,
            "timestamp": time.time(),
        }
        with open(self.facts_file, "a") as f:
            f.write(json.dumps(entry) + "\n")

    def store_rule(self, rule: str, trigger: str, derived_from: str):
        """Store a rule derived from experience."""
        entry = {
            "type": "rule",
            "content": rule,
            "trigger": trigger,
            "derived_from": derived_from,
            "timestamp": time.time(),
        }
        with open(self.rules_file, "a") as f:
            f.write(json.dumps(entry) + "\n")

    def retrieve_relevant(self, query: str, limit: int = 10) -> list[dict]:
        """Retrieve memories relevant to the current context."""
        all_memories = self._load_all()
        # In production, use vector embeddings for semantic search
        # This simplified version uses keyword matching
        scored = []
        for memory in all_memories:
            score = self._relevance_score(query, memory["content"])
            if score > 0:
                scored.append((score, memory))
        scored.sort(reverse=True, key=lambda x: x[0])
        return [m for _, m in scored[:limit]]

The Consolidation Problem

Raw memory accumulates fast. After a hundred sessions, you have thousands of facts, many redundant, some contradictory, most irrelevant to the current task. Retrieving everything is expensive. Retrieving nothing defeats the purpose.

The solution is consolidation -- periodically compressing raw memories into summaries, deduplicating, resolving contradictions, and promoting high-value memories while archiving low-value ones. Nevo's memory architecture uses a brain-inspired pipeline for this: a sensory buffer captures raw events, hippocampal encoding extracts meaningful facts, and neocortical consolidation merges them into stable long-term knowledge. For a deep dive on this approach, see How Nevo's Memory Architecture Works.

The key insight: memory is not a database you write to and read from. It is a living system that must be curated, compressed, and maintained -- or it degrades into noise that actively harms agent performance.

Injecting Memory into the Agent Loop

Memory only helps if the agent can access it at the right time. Modify your agent loop to retrieve and inject relevant memories:

def run(self, goal: str) -> str:
    # Retrieve relevant memories before starting
    relevant_memories = self.memory.retrieve_relevant(goal)
    memory_context = self._format_memories(relevant_memories)

    messages = [
        {"role": "system", "content": self.system_prompt()},
        {"role": "system", "content": f"Relevant context:\n{memory_context}"},
        {"role": "user", "content": goal},
    ]

    # ... rest of agent loop ...

    # After completion, store new learnings
    self.memory.store_fact(
        fact=f"Completed task: {goal}",
        source="agent_loop",
        importance=0.3,
    )

Step 5: Add Quality Gates

Here is where most tutorials end and most production systems fail. Your agent can reason, use tools, and remember context. But can you trust its output?

Without verification, you are shipping the first draft of an LLM's reasoning directly to your user. Sometimes that draft is brilliant. Sometimes it hallucinates a function that does not exist, misses an edge case, or introduces a subtle bug that will not surface for weeks.

Quality gates are automated verification stages that every output must pass before it reaches a user. They are the difference between "it generated something" and "it generated something correct."

The Quality Pipeline

A production-grade quality pipeline runs multiple verification stages in sequence. Each stage catches a different class of defect:

class QualityPipeline:
    def __init__(self):
        self.stages = [
            TypeChecker(),
            TestRunner(),
            Linter(),
            CodeCritic(),
        ]

    def run(self, artifact: str, context: dict) -> QualityResult:
        """Run all quality stages. Return pass/fail with details."""
        results = []
        for stage in self.stages:
            result = stage.check(artifact, context)
            results.append(result)
            if result.is_blocking:
                return QualityResult(
                    passed=False,
                    blocking_stage=stage.name,
                    details=results,
                )
        return QualityResult(passed=True, details=results)

What Each Stage Does

Type checking catches type errors, undefined variables, and interface mismatches before runtime. For Python, this means running mypy or pyright. For TypeScript, tsc. Fast, cheap, and catches entire categories of bugs.

Testing verifies that the code does what it claims to do. The agent should write tests alongside its code, and the quality pipeline should run them. A test suite that passes gives you confidence. A test suite that fails tells you exactly what is broken.

Linting enforces style consistency and catches anti-patterns. This is less about correctness and more about maintainability. Code that passes a linter is code that other developers (and future agents) can read.

Code review is where it gets interesting. A separate LLM call -- ideally at a higher model tier -- reviews the output with fresh eyes. It checks for logic errors, security issues, performance problems, and architectural violations that automated tools miss. This is expensive but catches the bugs that matter most.

The Refinement Loop

When a quality gate fails, the agent should not just report the failure. It should fix the problem and try again:

def run_with_refinement(self, goal: str, max_refinements: int = 3) -> str:
    for attempt in range(max_refinements + 1):
        result = self.agent_loop.run(goal)
        quality = self.quality_pipeline.run(result, context={"goal": goal})

        if quality.passed:
            return result

        # Feed quality feedback back to the agent
        refinement_prompt = (
            f"Your output failed quality checks at stage: "
            f"{quality.blocking_stage}\n"
            f"Issues found:\n{quality.format_issues()}\n"
            f"Fix these issues and try again."
        )
        goal = refinement_prompt

    # After max refinements, escalate
    return self.escalate(result, quality)

Nevo's 8-stage quality pipeline -- WRITE, TYPECHECK, TEST, LINT, CRITIQUE, REFINE, ESCALATE, ARBITER -- is a production example of this pattern. Seven specialized agents participate in the chain. The arbiter, running on the most capable model tier, makes the final ship/no-ship decision. It is not optional. It is what makes the output reliable enough to trust without manual review.

The Error-to-Rule Pipeline

Quality gates catch errors. But catching the same error twice is a waste. The next level is preventing error classes entirely.

When your system encounters a novel error, capture it, analyze the root cause, and encode a preventive rule:

def handle_quality_failure(self, error: QualityError):
    """Convert errors into preventive rules."""
    # Check if this error class has been seen before
    if self.rules_db.has_rule_for(error.category):
        return  # Already handled

    # Analyze root cause
    root_cause = self.analyze_root_cause(error)

    # Generate preventive rule
    rule = self.generate_rule(root_cause)

    # Store and activate the rule
    self.rules_db.add(rule)
    self.agent_loop.add_constraint(rule)

This is the mechanism that turns a static system into a self-improving one. Nevo calls it the error-to-rule pipeline: every unique mistake triggers root cause analysis, the finding is distilled into a 1-3 sentence preventive rule, and that rule is permanently wired into the system. That class of error becomes structurally impossible to repeat.

Step 6: Deploy to Production

A system that only runs on your laptop is a prototype. Deploying to production means solving reliability, observability, and operational concerns that do not exist in development.

Process Management

Your agent system needs to run as a persistent service, not a script you start manually:

# agent_service.py -- Entry point for the agent system
import signal
import sys

class AgentService:
    def __init__(self):
        self.agent = build_agent()  # All the components from steps 1-5
        self.running = True

        signal.signal(signal.SIGTERM, self._shutdown)
        signal.signal(signal.SIGINT, self._shutdown)

    def _shutdown(self, signum, frame):
        self.running = False
        self.agent.memory.flush()
        sys.exit(0)

    def run(self):
        """Main service loop -- process incoming requests."""
        while self.running:
            request = self.queue.get()
            try:
                result = self.agent.run(request.goal)
                request.respond(result)
            except Exception as e:
                self.handle_error(e, request)

On Linux, use systemd. On macOS, use launchd. On a server, use Docker with a process manager. The key requirement: your agent restarts automatically after crashes, persists logs, and flushes memory before shutdown.

Observability

You cannot improve what you cannot measure. Instrument your agent system with:

• Token usage per task -- Know what each task costs. Identify expensive patterns.
• Latency per stage -- Find bottlenecks in your quality pipeline.
• Error rates by type -- Track which errors are decreasing (rules working) and which are new.
• Memory growth -- Monitor the size and retrieval quality of your memory system.
• Quality gate pass rates -- Measure how often the agent produces correct output on the first attempt.

class AgentMetrics:
    def record_task(self, task_id: str, metrics: dict):
        """Record metrics for a completed task."""
        self.store.append({
            "task_id": task_id,
            "tokens_used": metrics["tokens"],
            "latency_ms": metrics["latency"],
            "quality_attempts": metrics["refinement_count"],
            "model_tier": metrics["model"],
            "timestamp": time.time(),
        })

Security Considerations

An agent with tool access is powerful. That power requires constraints:

• Sandboxing -- Run tool executions in isolated environments. An agent that can write files should not be able to write to /etc/passwd.
• Approval gates -- Some actions (deleting data, sending emails, deploying code) should require human confirmation. Build approval workflows into your agent loop.
• Credential isolation -- Store API keys and tokens in a credential store, not in source code. Pass them to tools at runtime through environment variables or secret managers.
• Audit logging -- Record every tool call, every model input, every output. When something goes wrong, you need the full trace.

Scaling with Multi-Agent Orchestration

Once your single agent is reliable, the next step is specialization. Instead of one generalist agent, deploy multiple specialist agents coordinated by an orchestrator:

class Orchestrator:
    def __init__(self):
        self.agents = {
            "planner": PlannerAgent(),
            "coder": CoderAgent(),
            "reviewer": ReviewerAgent(),
            "deployer": DeployerAgent(),
        }

    def execute(self, goal: str):
        plan = self.agents["planner"].decompose(goal)

        for task in plan.tasks:
            agent = self.agents[task.assigned_to]
            result = agent.run(task)

            # Every coding output goes through review
            if task.type == "code":
                review = self.agents["reviewer"].review(result)
                if not review.approved:
                    result = agent.refine(result, review.feedback)

        return self.compile_results(plan)

Nevo operates with 21 specialized agents, each purpose-built for a specific discipline -- type checking, testing, linting, code review, security analysis, incident investigation, and more. The orchestrator routes tasks based on complexity and type, and the quality pipeline ensures every output meets a consistent standard. For a comparison of orchestration approaches, see AI Agent Frameworks Compared.

Putting It All Together

Here is the complete architecture, from LLM to production deployment:

User Goal
    |
    v
[Orchestrator] -- routes to appropriate agent(s)
    |
    v
[Agent Loop] -- perceive, reason, act, observe
    |               |
    |               v
    |         [MCP Tools] -- file system, APIs, databases, web
    |               |
    |               v
    |         [Memory] -- retrieve relevant context, store new learnings
    |
    v
[Quality Pipeline]
    |
    TYPECHECK --> TEST --> LINT --> CRITIQUE
    |
    v
[Pass?] --no--> [Refine + Retry]
    |
   yes
    |
    v
[Error-to-Rule] -- novel errors become preventive rules
    |
    v
[Deliver Result]

Each layer adds a specific capability:

Common Mistakes

Having built and operated a production agent system, here are the mistakes we see most often:

Starting with a framework instead of understanding the primitives. Frameworks abstract away the agent loop, tool calling, and memory management. That is their value and their danger. If you do not understand what the framework is doing for you, you cannot debug it when it breaks. Build from scratch first, then evaluate whether a framework adds value. See AI Agent Frameworks Compared for an honest assessment.

No termination conditions. An agent without circuit breakers will loop until it exhausts your token budget. Define max iterations, error thresholds, and token limits before your first production run.

Treating memory as append-only storage. Raw memory grows without bound and degrades retrieval quality. You need consolidation, deduplication, and relevance scoring -- not just a growing list of facts.

Skipping quality gates to move faster. Every team that skips verification to "ship faster" ends up shipping bugs faster. The quality pipeline is not overhead. It is the mechanism that makes your agent's output trustworthy.

Building everything custom when MCP servers exist. Check the MCP ecosystem before building another file system wrapper or database connector. The protocol exists to prevent this exact duplication of effort.

Single-model architecture at scale. Using your most capable model for every task is like hiring a senior architect to fix typos. Model routing is not premature optimization -- it is the difference between a system that costs $50/day and one that costs $500/day for the same output quality.

What This Architecture Becomes

The system described in this guide is not a theoretical exercise. It is the architecture behind Nevo -- a self-improving AI agent system that coordinates 21 specialized agents through an 8-stage quality pipeline, learns from every mistake through the error-to-rule pipeline, and writes its own new capabilities when it identifies gaps.

But Nevo started exactly where you are starting now: a single agent loop calling a single model with a few tools. Every layer was added because a real problem demanded it. Memory was added because sessions were not compounding. Quality gates were added because first drafts were not reliable enough. The error-to-rule pipeline was added because the same mistakes kept recurring.

You do not need to build all six layers on day one. Start with steps 1 and 2 -- a model and an agent loop. Get that working. Then add tools. Then memory. Then quality gates. Each layer solves a real problem you will encounter as you operate the system, and you will understand why it matters because you felt the pain of not having it.

The best AI agent systems are not the ones with the most features. They are the ones that get better every day. Build the feedback loops -- quality gates, error-to-rule, memory consolidation -- and the system will improve itself faster than you can improve it manually.

For a deeper look at how this architecture works in production, explore Nevo: The Self-Improving AI Agent. To understand the infrastructure layer that makes tool use universal, see What Is OpenClaw?

Frequently Asked Questions

What programming language should I use to build an AI agent system?

Python is the most practical choice in 2026. The AI ecosystem -- model SDKs, MCP libraries, embedding models, vector stores -- is overwhelmingly Python-first. TypeScript is a strong second choice, especially if your agent system integrates with web services. The architecture described in this guide is language-agnostic, but the available tooling favors Python.

How much does it cost to run an AI agent system?

Costs depend on model choice, task complexity, and volume. A single agent running Claude Sonnet on moderate workloads costs roughly $5-15/day. Model routing (using cheaper models for simple tasks) can reduce this by 60-80%. Quality gates add cost per task but reduce rework costs significantly. Budget for experimentation during development -- you will iterate on prompts, tools, and pipeline stages.

Do I need a framework like LangChain or CrewAI?

Not to start. Frameworks add value when you need production features like state persistence, human-in-the-loop workflows, or pre-built integrations. But they also add abstraction layers that obscure what your agent is actually doing. Build the core loop yourself first. Understand the primitives. Then evaluate frameworks against your specific needs.

How do I test an AI agent system?

Test at three levels. Unit test individual tools and utility functions. Integration test the agent loop with mocked model responses and real tools. End-to-end test with real models against known-good task/result pairs. Record model outputs during development so you can replay them in CI without API costs.

What is the minimum viable agent system?

An LLM, an agent loop with termination conditions, and one tool. That is enough to demonstrate autonomous behavior. From there, add memory (so sessions compound), quality gates (so output is reliable), and more tools (so the agent can do more). Each addition should solve a problem you are actually experiencing.

How is an AI agent system different from a chatbot?

A chatbot responds to messages. An AI agent system pursues goals. The difference is autonomy: an agent decomposes complex objectives into subtasks, uses tools to interact with external systems, remembers context across sessions, verifies its own output through quality gates, and improves its behavior over time through feedback loops. A chatbot is a single model behind an API. An agent system is an architecture. For a detailed comparison, see AI Agent vs. Chatbot.