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An LLM agent is a model-centered system that chooses actions, calls tools and advances a multi-step task toward a goal. Build one reliably by starting with a bounded use case, using the simplest control flow that works, exposing narrowly scoped tools, persisting only necessary state, requiring approval for consequential actions, and evaluating the complete trajectory before deployment.
What makes a system an agent?
A conventional application follows a workflow you specify in advance. An agent can decide which step to take next on a user’s behalf, often repeating a reason–act–observe cycle until it reaches a defined outcome or a stop condition. A single prompt followed by one generated answer is an LLM feature, not automatically an agent.
Define the boundary in operational terms:
- Goal: the result the user is asking for.
- Authority: what the system may read, change or send.
- Success: a testable condition, not merely a plausible-sounding response.
- Failure cost: what happens when the model is wrong, a tool is unavailable or an instruction is malicious.
- Stop conditions: maximum turns, elapsed time, spend, repeated errors and required human review.
Good first projects include research with citations, drafting and revising content, customer-support triage, coding assistance and structured back-office work. Avoid an open-ended “do anything” agent until you can measure a narrower workflow.
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Choose the smallest architecture that can succeed
Start with an augmented LLM
Give the model only the context, retrieval and tools needed for the task. Keep the first version as a single model call plus deterministic application code around it. Anthropic’s engineering guidance recommends increasing complexity progressively: augmented LLM first, compositional workflow next, autonomous agent last.
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Use a workflow when the path is predictable
Many teams call a sequence of model and software steps an agent, but a deterministic workflow is often safer and cheaper. Use ordinary code for validation, formatting, retries, access control and calculations. Let the model handle interpretation and choices where language is genuinely required.
Select a control-flow pattern deliberately
| Pattern | Use it when | Main trade-off |
|---|---|---|
| Sequential | Each step depends on the previous result, such as extract, classify, then draft. | Easy to trace, but latency grows with every step. |
| Routing | Requests fall into distinct specialist paths. | Improves focus; a bad route can send the task to the wrong capabilities. |
| Parallel | Independent searches or analyses can run at the same time. | Reduces wall-clock time, while increasing coordination and spend. |
| Evaluator–optimizer | A draft can be reviewed against explicit criteria and revised. | Useful for quality-sensitive work; loops need strict limits. |
| Autonomous loop | The next action cannot be known in advance and depends on observations. | Most flexible and hardest to bound, test and secure. |
Google ADK documents sequential, parallel and loop workflow agents. Anthropic documents evaluator–optimizer patterns. Treat these as composable control-flow primitives, not as a reason to add agents everywhere.
Design tools as safe, typed interfaces
A tool is an API boundary, not a paragraph in a system prompt. Give each tool one job, an explicit name and a narrow schema. Describe valid inputs, units, permissions, side effects and failure responses. Prefer structured return fields such as status, items and next_cursor over arbitrary prose.
Keep permissions least-privilege
- Issue separate credentials for read and write operations.
- Restrict records, repositories, buckets and domains to the task’s scope.
- Set server-side limits for amount, recipient, query size, rate and execution time.
- Make destructive operations separate tools rather than optional flags on a broad tool.
- Log the caller, arguments, authorization result and external request ID.
Separate untrusted data from instructions
Retrieved documents, web pages, emails and tool responses are data. Put them in clearly delimited fields and never concatenate them into a higher-priority instruction. Use structured extraction so arbitrary text cannot directly become a tool name or argument. Validate every argument again in application code; the model’s schema adherence is not an authorization check.
Example tool contract
{
"name": "get_invoice",
"description": "Read one invoice belonging to the authenticated workspace. Does not modify data.",
"input_schema": {
"type": "object",
"properties": {
"invoice_id": {"type": "string", "pattern": "^inv_[A-Za-z0-9]+$"}
},
"required": ["invoice_id"],
"additionalProperties": false
}
}Return a typed error such as not_found, forbidden or rate_limited. The agent can then choose a recovery path instead of trying to interpret an ambiguous sentence.
Implement the agent loop
The core loop is small: send the conversation and available tools to the model, execute only validated calls, append observations, and stop on a final answer, an approval requirement or a limit. Keep the model adapter separate from policy and tool execution so you can change providers without rewriting safety logic.
from dataclasses import dataclass
from typing import Any, Callable
@dataclass
class Tool:
name: str
description: str
schema: dict[str, Any]
handler: Callable[[dict[str, Any]], dict[str, Any]]
requires_approval: bool = False
class Agent:
def __init__(self, model_call, tools: list[Tool], max_turns: int = 8):
self.model_call = model_call
self.tools = {tool.name: tool for tool in tools}
self.max_turns = max_turns
def run(self, user_text: str, state: dict[str, Any] | None = None) -> str:
messages = [{'role': 'user', 'content': user_text}]
state = state or {}
for turn in range(self.max_turns):
response = self.model_call(messages, list(self.tools.values()), state)
if response['type'] == 'final':
return response['text']
if response['type'] != 'tool_call':
raise ValueError('Model returned an unsupported response type')
name = response['name']
tool = self.tools.get(name)
if tool is None:
messages.append({'role': 'tool', 'content': {'error': 'unknown_tool'}})
continue
arguments = response.get('arguments', {})
validate_arguments(tool.schema, arguments)
if tool.requires_approval and not request_human_approval(name, arguments):
return 'The operation was not approved.'
try:
result = tool.handler(arguments)
except Exception as exc:
result = {'error': 'tool_failed', 'detail': str(exc)}
messages.append({'role': 'assistant', 'tool_call': response})
messages.append({'role': 'tool', 'name': name, 'content': result})
return 'The task stopped after reaching its turn limit.'
def validate_arguments(schema: dict[str, Any], arguments: dict[str, Any]) -> None:
required = set(schema.get('required', []))
if not required.issubset(arguments):
raise ValueError('Missing required tool argument')
if schema.get('additionalProperties') is False:
allowed = set(schema.get('properties', {}))
if set(arguments) - allowed:
raise ValueError('Unexpected tool argument')
def request_human_approval(name: str, arguments: dict[str, Any]) -> bool:
print(f'Approve {name} with {arguments}? [y/N]')
return input().strip().lower() == 'y'Implement model_call with the SDK or API of your chosen model. It should return either {'type': 'tool_call', ...} or {'type': 'final', 'text': ...}. In production, replace the simple validator with a JSON-Schema implementation, redact secrets from logs and make the approval service independent of the model process.
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Persist task state, not everything
Keep an explicit state object containing the task ID, authenticated principal, completed steps, tool results needed for later decisions and a deadline. Store durable business records in your normal database. Treat conversation history as an input with a size and retention policy, not as an unlimited memory.
Separate short-lived working state from long-term memories. A memory should have an owner, purpose, source, timestamp and deletion path. Never let a retrieved memory silently expand the agent’s authority.
Gate consequential effects
Require confirmation before purchases, external messages, permission changes, production deployments, deletions or other irreversible writes. Show the exact operation, target, arguments and expected consequence. Keep approvals enabled even when a tool call looks routine; OpenAI’s safety guidance recommends user review for operations with side effects.
Make cancellation real
Pass a cancellation token through every tool, enforce timeouts at the network layer and expose an emergency stop that revokes credentials or terminates the job. A UI stop button that only hides output is not a safety control.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsGive agents visual web tools without making a browser part of every worker
Some agents need a rendered page rather than raw HTML: visual QA, documentation capture, evidence collection or checking a user-facing flow. Define a tool such as capture_page(url, format, full_page), restrict allowed domains, and store the resulting object in controlled storage. Treat screenshots as untrusted observations; they can contain instructions aimed at the model.
ScreenshotNeo provides a website screenshot API and MCP server for this use case. It can accept consent banners before capture and remove more than 60 known consent platforms, newsletter popups and chat widgets; each step can be disabled. Clean shots are the only billable responses, while bot checks or CAPTCHAs, blank pages, timeouts, failed loads and cache hits are reported in response headers and cost nothing. Its MCP tools include take_screenshot, get_page_info and capture_pdf, so Claude, Cursor or another MCP client can call it directly.
Or skip the browser setup:
Use one HTTP request from a tool handler. The endpoint supports PNG, JPEG, WebP and PDF output, and ScreenshotNeo offers options for full-page and element capture, device and retina settings, dark mode, custom CSS and JavaScript, waits, request blocking, headers, cookies, user agents, geolocation, caching, signed links, asynchronous jobs, bulk capture and usage reporting.
cURL:
curl -G "https://api.screenshotneo.com/v1/shot" -d access_key=YOUR_API_KEY --data-urlencode url=https://stripe.com -o shot.webpPython:
import requests
r = requests.get('https://api.screenshotneo.com/v1/shot', params={'access_key': 'YOUR_API_KEY', 'url': 'https://stripe.com'}, timeout=90)
r.raise_for_status()
open('shot.webp', 'wb').write(r.content)Node.js:
const q = new URLSearchParams({ access_key: 'YOUR_API_KEY', url: 'https://stripe.com' });
const res = await fetch(`https://api.screenshotneo.com/v1/shot?${q}`);
if (!res.ok) throw new Error(`HTTP ${res.status}`);
require('fs').writeFileSync('shot.webp', Buffer.from(await res.arrayBuffer()));See the ScreenshotNeo API documentation for parameter names and response headers. Cookie banners, popups and chat widgets are removed before the shot; bot checks, blank pages and failed loads are never billed. The MCP server lets AI agents take screenshots. The Free plan includes 1,000 screenshots a month with no card, and paid plans start at $5 for 3,000 shots. Create a free ScreenshotNeo account.
Evaluate the whole trajectory
Testing only the final answer misses the failures that matter. Build evaluation cases with realistic users, ambiguous requests, malformed tool results, permission denials, prompt-injection attempts, rate limits and partial outages. Score:
- Whether the agent selected the permitted tool.
- Argument correctness and adherence to schema and policy.
- State transitions and recovery after an error.
- Whether it requested approval at the right point.
- Final-result correctness, completeness and citation or evidence quality.
- Turn count, latency, token usage and tool cost.
Use deterministic fixtures for external systems, then run multi-turn tests in a sandbox where the agent can change state. OpenAI provides agent-evaluation and trace-grading surfaces; Anthropic describes evaluations in which an agent uses tools over multiple turns and changes an environment. Keep a regression set for every prompt, tool, model and policy change.
Choose a platform by workload, not brand
| Option | What it provides | Questions to answer |
|---|---|---|
| OpenAI agent tooling | Direct model calls, custom tools and workflows, plus managed support for long-running tasks. | Can your data, approval flow and observability requirements fit the managed execution model? |
| Google Agent Development Kit | Open-source multi-agent workflow primitives; Google’s managed runtime can deploy ADK, LangGraph, LangChain, AG2 or LlamaIndex agents. | Do you need portability across those frameworks, and which runtime region and data controls apply? |
| Anthropic Claude API and patterns | Vendor-neutral workflow patterns and detailed tool-design guidance centered on Claude models. | Which model, context limits and hosting arrangement meet your latency and privacy requirements? |
Compare model capability, protocol and tool support, orchestration control, state and memory, deployment target, observability, evaluation support, safety controls, latency and total cost. A framework that hides these decisions can make a prototype quick but production debugging difficult.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Deploy with observability and rollback
Record a trace ID for every run and link model turns, tool calls, approvals, retries, errors and user outcome to it. Capture latency by step, token and tool costs, queue time, timeouts, policy violations and cancellation rate. Redact credentials and sensitive payloads before logs leave the trust boundary.
Use queues and idempotency keys for long-running jobs. Retry only transient failures with exponential backoff and a maximum attempt count. Cache safe, immutable reads; never cache a response that depends on authorization without including the authorization scope in the cache key. Keep a deterministic fallback for high-impact steps and roll back model, prompt, tool or policy changes independently.
OpenAI’s safety documentation says Agent Builder is scheduled to shut down on November 30, 2026. Verify its current status before making it a new dependency; do not design a production architecture around a tool that is nearing retirement.
Common failures and fixes
The agent loops or repeats a tool call
Add a per-run turn limit, detect identical calls, return structured error states and require a changed argument or explicit escalation before retrying.
The model invents a successful result
Require a tool observation for every external fact, distinguish pending, succeeded and failed states, and prevent the final response until required fields are present.
A tool call is valid JSON but unsafe
Validate authorization, target ownership, amount and side-effect policy after schema validation. Put limits in the tool server, not only in the prompt.
Prompt injection changes the plan
Keep external text in a data field, isolate retrieval from instructions, filter or quarantine suspicious content and require approval for any newly proposed privilege or destination.
Latency or cost is unpredictable
Measure each model and tool span, parallelize independent reads, cap context and loop length, cache safe results and route simple requests to a smaller model or deterministic workflow.
The browser capture is blank or blocked
Check the page verdict and X-Billed response headers, increase the wait condition or delay, select the correct viewport, and treat bot checks and failed loads as recoverable tool outcomes rather than valid evidence.
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A production checklist
- Write the goal, authority, success test, failure cost and stop conditions.
- Implement a deterministic workflow or augmented LLM before adding autonomy.
- Define narrow, typed tools with least-privilege credentials and server-side validation.
- Isolate untrusted content and add input, output and PII guardrails.
- Persist only task state that is required and give memories retention and deletion rules.
- Gate irreversible actions with an explicit human approval screen.
- Evaluate complete trajectories in a sandbox, including attacks and outages.
- Instrument traces, costs, latency, approvals, errors and user outcomes.
- Deploy with cancellation, idempotency, bounded retries, fallbacks and rollback.
Frequently Asked Questions
Should I build a multi-agent system first?
No. Start with one augmented LLM or a deterministic workflow. Split into specialists only when routing, isolation or parallel work provides a measurable benefit.
What is the safest default for a new write operation?
Expose it as a separate, narrowly scoped tool that requires explicit user approval and enforces authorization and limits on the server.
How do I compare agent frameworks objectively?
Run the same trajectory test set and compare tool correctness, recovery, latency, observability, evaluation support, safety controls and total cost—not just model quality.
Can an agent use screenshots as evidence?
Yes, but treat images as untrusted observations, restrict capture domains, record the capture metadata and require independent checks before consequential actions.
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