...build an agent

From PRIMO.ai
Jump to navigation Jump to search
↺ Start over · Full treeYou are here: AI Solver › Generate and converse › ...build an agent
AI Solver map
Your current page is in bold
0 · Start
1 · Predict & analyze
tables, numbers, events
2 · Understand unstructured data
text, documents, images, audio
3 · Generate & converse
4 · Decide & act
5 · Build it right
applies to every path

>>>> Start here

Use this page when you want AI to do things, not just answer: file a ticket, update a CRM, research a topic, fix code, process a claim. An agent is a model using tools in a loop toward a goal. Agents are powerful but slower, costlier and riskier than simpler designs, so add autonomy step by step.

  1. Can one well-designed model call do it (with retrieval or a single tool)? → Yes: stop here; that is not an agent, and that is good → ...answer questions from my data
  2. Are the steps known in advance? → build a workflow: your code controls the path, the model fills in each step
    • Fixed sequence → prompt chaining (draft → check → translate)
    • Different inputs need different handling → routing (easy questions to a small model, hard ones to a large model)
    • Independent parts, or several opinions → parallelization (split the work, or vote)
    • Subtasks depend on the input → orchestrator–workers (one model plans, others execute)
    • Quality improves with critique against clear criteria → evaluator–optimizer loop
  3. Steps cannot be predicted, the task is valuable, and mistakes can be caught or undone? → autonomous agent: the model plans, calls tools, checks results and repeats. Add stop conditions, time and cost budgets, and human approval for risky actions
  4. Must it operate software that has no API? → computer-use or browser agents that click and type. They are slower and more brittle; use APIs or MCP connectors whenever they exist
  5. Is it software work? → coding agents (Claude Code, OpenAI Codex, GitHub Copilot coding agent, Cursor, Gemini CLI); give them tests to run and an AGENTS.md file with project instructions
  6. Is one agent failing because it has too many tools or domains? → multi-agent: a manager agent that delegates, or agents that hand off to each other; across vendors, connect them with the A2A protocol. Expect higher cost and harder debugging


Which AI agent environment should I use?

Artificial intelligence agents can research information, perform work, operate applications, automate business processes, develop software, and coordinate complex tasks. The right environment depends on what you want to accomplish, how much technical work you want to undertake, and how much responsibility you want to delegate to AI.

This guide helps you select among five approaches:

  1. Use an existing AI agent to perform personal, professional, or organizational work.
  2. Build a visual or low-code workflow to automate applications and configure custom agents.
  3. Use an AI coding agent to create, modify, debug, or maintain software.
  4. Develop a custom agent using programming frameworks, SDKs, or managed services.
  5. Choose models and infrastructure to supply intelligence and execution services to an agentic application.

Start with the task and its data privacy requirements, before choosing a vendor. Define the permitted data boundary in Step 0, then identify the simplest eligible environment that reliably accomplishes the task.

This article is the central entry point for the AI Solver agent-environment selection system. Four dedicated comparison articles provide detailed product profiles, official quickstarts, and hands-on exercises. This selector maintains the overall decision process and the common evaluation scorecard.

Product landscape reviewed: October 29, 2026. Vendor features, pricing, availability, and documentation change frequently.

Quick Navigation

Step 0: Mandatory Data Privacy Gate

Before selecting a product, connecting an account, uploading files, indexing a repository, or running a quickstart with real information, establish which data the complete system is permitted to process and where it may go.

Define the intended task and its minimum necessary information first. Privacy is an eligibility requirement for every path, including ready-to-use agents, visual builders, coding agents, custom frameworks, and infrastructure. Feature scores, convenience, and price cannot compensate for an unmet privacy requirement.

Classify the Information and Define the Boundary

Use your organization's classification scheme where one exists. The categories below are planning examples, not legal classifications or automatic permission to use a service.

Information involved Starting decision
Public or synthetic information A broadly available service may be suitable, but still check account metadata, credentials, prompts, outputs, and connected resources. Public source material can become sensitive when combined with a private question or business objective.
Internal business information Require an approved product, account, and configuration for that information. Limit connector scope, sharing, retention, and provider use to the agreed purpose.
Confidential information, personal data, or proprietary source code Establish the permitted recipients, processing locations, access controls, retention, and contractual terms before disclosure. An enterprise subscription alone is not evidence that every feature qualifies.
Restricted information or information subject to specific contractual or regulatory obligations Obtain the required data-owner and organizational review for the exact use case. Exclude any configuration whose providers, locations, features, or controls cannot satisfy the applicable requirements.
Information that must remain within a defined local, private, or disconnected boundary Use only a verified architecture that keeps every relevant processing and storage component within that boundary. If no candidate meets it, stop or redesign the task.

For mixed inputs, apply the most restrictive applicable handling requirements unless separation or transformation has been reviewed and shown to remove the restricted information. Removing names alone does not establish that data is anonymous. Keep live credentials out of prompts and sample files; supply necessary authentication through approved credential mechanisms.

Map the Complete Data Flow

Record what moves from user and source systems → agent client or runtime → model service → tools and connectors → storage and monitoring → output recipients. Include return traffic and any additional providers used by routing or fallback.

Account for prompts, uploaded files, repository contents, retrieved passages, screenshots, browser sessions, tool arguments and results, outputs, memory, embeddings, caches, checkpoints, traces, telemetry, support access, backups, and exports. A read-only task can still disclose information. Approval to perform an external action is separate from permission to send its input to a model or service.

Check the Mandatory Privacy Requirements

Complete this checklist for the exact product, edition, account, region, endpoint, model, tools, and configuration being considered. Keep the evidence in a private project record.

Requirement Evidence required for the intended data
Purpose and minimum data Document what information is necessary, who may authorize its use, and what can be omitted, replaced with synthetic data, or transformed before processing.
Permitted recipients and provider use Identify the platform, model providers, subprocessors, connected services, and human support or review access. Verify training, service-improvement, feedback, and opt-in settings under the applicable terms.
Processing and storage locations Confirm allowed locations separately for inference, tool execution, stored content, logs, backups, and support access. Storage residency alone does not establish processing residency.
Retention and deletion Record retention periods, exceptions, deletion procedures, and coverage for conversations, files, memory, indexes, traces, caches, and backups. A no-training commitment does not mean no retention; deleting a visible chat does not establish deletion of every associated copy.
Identity, access, and separation Verify least-privilege access, user and tenant separation, connector scopes, sharing, authentication, credential protection, and required encryption. Encryption does not by itself authorize a disclosure.
Outbound transfers and integrations Check model routing, fallback providers, search queries, webhooks, MCP servers, plugins, telemetry, and export destinations. Enforce any required restrictions in application, platform, or network controls.
Contractual and organizational requirements Retain the applicable service terms, data-processing terms and required agreements, internal approvals, and any feature or regional exclusions. Confirm their scope with the responsible organizational reviewers where required.
Verification and operational ownership Keep dated documentation, configuration evidence, and appropriate synthetic-data test results. Assign an owner for monitoring, revocation, deletion requests, incident handling, and reassessment.

Provider documentation and contractual commitments address matters that cannot be proven by a short functional test. Tests establish observed behavior for the tested configuration; they do not prove every backend process or future execution.

Choose an Eligible Deployment Pattern

Pattern to investigate Privacy boundary to verify
Consumer or general-purpose hosted service Determine whether its exact terms and settings permit the intended information. Do not infer organizational approval from personal access to the service.
Approved enterprise service or managed API Verify the purchased edition, contract, enabled features, regions, connected services, and administrative settings. Approval of one product does not automatically cover another product from the same provider.
Self-hosted runtime with remote models or tools The runtime is under your control, but each remote model, search service, connector, and telemetry destination remains a separate data recipient.
Private or local runtime with private or local inference Verify the model endpoint, tools, storage, monitoring, backups, and administrative access. Disable or restrict external services where required and test the resulting configuration.
Disconnected execution Verify that the runtime and all necessary dependencies can operate without external connections; maintain controlled updates, imports, exports, and operational procedures.

These are architectural options, not a ranking of privacy quality. Open-source software, a local interface, self-hosting, a private network, or an enterprise label is not sufficient evidence of compliance with your requirements.

Record the Gate Decision

Status Meaning Next action
Pass Every applicable mandatory privacy requirement has supporting evidence for the defined data and configuration. Proceed to the questionnaire and compare eligible candidates. Retain the approved scope and evidence.
Fail At least one mandatory requirement is not met. Exclude this configuration. Change the architecture, data, or controls and reassess before proceeding.
Unverified Evidence is missing, ambiguous, or not applicable to the selected edition, feature, region, or endpoint. Keep the candidate out of the approved shortlist for the intended data. Seek evidence; any exploratory trial must be separately permitted and limited to public or synthetic inputs without sensitive account connections.

The project gate passes only when all applicable privacy requirements pass. Mark a requirement not applicable only with a documented reason. A workaround must actually meet the requirement and be verified; documenting a failure does not convert it into a pass.

Record the data classification, permitted purposes and destinations, exact configuration, evidence dates, gate status, reviewer where required, and reassessment triggers. Re-run the gate when the data, model, account, connector, region, retention policy, provider terms, or execution architecture changes.

Next: After defining the privacy boundary, use Step 1 to select an approach. Apply the gate to each concrete candidate before shortlisting it for the intended data.

Step 1: Interactive Agent Environment Questionnaire

Which agentic AI environment is right for your project?

First define the information and permitted deployment boundary using the mandatory privacy gate. Follow this clickable questionnaire to identify the most appropriate development approach, then evaluate only candidate configurations that pass the gate for that scope. The product examples below indicate functional fit; they are not privacy approvals.

The questionnaire uses ordinary MediaWiki links and does not require JavaScript, account registration, or additional software.

Question 1: What do you want AI to accomplish?

Your objective Follow this path
I want AI to carry out personal or professional tasks. A. Use an existing agent
I want to automate connected applications and business workflows. B. Automate applications and workflows
I want AI to help create, debug, or maintain software. C. Use an AI coding agent
I want to build an application that contains custom AI agents. D. Develop a custom agent
I need models, inference services, or supporting agent infrastructure. E. Choose models and infrastructure

Not sure which path to choose?

Consider the deliverable:

  • If the result is a completed assignment, start with Path A.
  • If the result is a repeatable automation, start with Path B.
  • If the result is software written with AI assistance, start with Path C.
  • If the result is software containing an AI agent, start with Path D.
  • If the result is technical infrastructure for an agentic system, start with Path E.

A project may eventually combine several paths. For example, an AI coding agent from Path C might help develop an application that uses an SDK from Path D and a model service from Path E.

Path A: Use an Existing Agent

Question 2: What kind of work do you want to delegate?

Your primary requirement Examples worth investigating
Maintain ongoing responsibilities, monitor information, or run recurring tasks. OpenAI dots; Gemini Tasks; Claude Duet; Microsoft Copilot Autopilot; Grok Bot; Grok Automations
Research information, prepare documents, analyze data, and complete professional assignments. ChatGPT Work; Claude Cowork; Microsoft Copilot Cowork; Perplexity Computer
Interact with websites or graphical applications. Perplexity Comet; Meta Muse
Produce professional research with supporting sources. You Research Agent / ARI
Assist through personal devices and supported applications. Apple Siri AI

Your next destination:

Ready-to-Use AI Agent Comparison

The detailed guide explains the available agent categories, individual product capabilities, access requirements, official setup resources, practical exercises, and operating considerations.

Choose this path when: An existing application can complete the work without requiring you to build or maintain an agent system.

Key question: Can the agent produce the actual result and take the permitted actions you need, rather than merely explain how to do the work?

Path B: Automate Applications and Workflows

Question 2: How much development and configuration do you want to do?

Select the approach most appropriate for your needs:

B1: Minimal Programming

Your goal: Connect applications and configure AI-assisted workflows using visual tools.

Initial candidates:

  • Make AI Agents — Visual scenarios combining AI agents and connected application operations.
  • AI by Zapier — AI steps and tool use within Zapier automations.
  • Microsoft Copilot Studio — Low-code business agents and supported workflows.

Choose this approach when: Required applications already have suitable connectors and most of the process can be configured graphically.

Key question: Can the workflow be built and maintained without custom programming?

B2: Flexible Workflow Development

Your goal: Combine visual orchestration with APIs, scripting, advanced data transformation, or custom logic.

Initial candidates:

  • n8n — Visual workflows with API access, scripting, AI tools, and cloud or self-hosted deployment.
  • Make AI Agents — Connected application workflows with configurable AI decisions and tools.
  • Baidu Qianfan — Enterprise-oriented agent and workflow development within the Baidu ecosystem.

Choose this approach when: Application integrations or business rules require additional technical flexibility.

Key question: Can the environment handle your custom logic, exceptions, integrations, and deployment requirements?

B3: Enterprise Agent Automation

Your goal: Develop agents using organizational information, identity management, access controls, and enterprise policies.

Initial candidates:

  • Microsoft Copilot Studio — Microsoft 365, Power Platform, business connectors, and organizational governance.
  • Gemini Enterprise Agent Designer — Google-connected enterprise agents and visual workflows.
  • Baidu Qianfan — Enterprise agent development and knowledge applications using Baidu services.

Choose this approach when: The agent must operate within an organization's controlled application and data environment.

Key question: Can the platform enforce the required identity, information access, approval, auditing, and administrative controls?

Your next destination for B1, B2, or B3:

Visual and Low-Code Agent Builder Comparison

The detailed guide provides platform profiles, official quickstarts, a common inquiry-processing exercise, deployment considerations, and selection guidance.

Important distinction: A predictable fixed workflow is often preferable to an autonomous agent. Use AI-based planning and tool selection only when the task requires that flexibility.

Path C: Use an AI Coding Agent

Question 2: Which software development experience do you prefer?

Development preference Initial candidates
Graphical development and agent supervision Google Antigravity; Claude Code
Terminal-based coding and repository maintenance Claude Code; Qwen Code; Muse Code
Open-source coding tools and configurable model access Qwen Code
Coding automation and supported CI workflows Claude Code; Qwen Code; Muse Code
Experimental agent runtime and plugin development DeepSeek Harness

Your next destination:

AI Coding Agent Comparison

This guide compares the development interfaces, model options, security controls, official installation instructions, and testing capabilities of five coding environments.

Choose this path when: Your goal is to create or maintain software with assistance from AI, including applications that are not themselves AI-powered.

Key question: Can the agent produce correct, maintainable source changes that you can test, review, and keep under version control?

Important: A coding agent is not necessarily the runtime that executes your completed application.

For other AI-assisted development approaches, see Codeless Options, Code Generators, Drag n' Drop.

Path D: Develop a Custom Agent

Question 2: What kind of application architecture do you need?

Development requirement Initial candidates
A code-first agent with tools, handoffs, and straightforward application integration OpenAI Agents SDK; Google ADK
Claude-based agent execution with files, commands, and application-defined permissions Claude Agent SDK
Explicit workflow state, branching, persistence, and execution control LangGraph
Specialized agents working through coordinated tasks and flows CrewAI
Qwen-based agent development and extensible tools Qwen-Agent
Provider-managed agent execution OpenAI Agents API; Claude Managed Agents; Gemini Enterprise Agent Platform
Specialized web research within an existing application Perplexity APIs; You.com Research API

Your next destination:

Agent Development Framework Comparison

The detailed guide distinguishes frameworks, SDKs, model APIs, managed agent execution, and research APIs. It also provides official quickstarts and a common research-and-approval application exercise.

Choose this path when: Your application requires agent behavior that existing products or visual builders cannot adequately provide.

Key question: Which components should your application control, and which should be provided by a managed service?

Important: A custom agent application may require tools, application state, user authentication, permissions, monitoring, and deployment infrastructure in addition to the AI model.

If you do not need this level of control, consider Path A or Path B instead.

Path E: Choose Models and Infrastructure

Question 2: Which part of an agentic system do you need?

  • Model intelligence: A model capable of reasoning, generating outputs, or selecting tools.
  • Inference service: An API or hosting environment that executes model requests.
  • Cloud infrastructure: Services for hosting applications, tools, databases, and workloads.
  • Managed agent infrastructure: Services that support agent execution, sessions, tools, or deployment.

A model or inference API is not automatically a complete agent environment.

Your next destination:

Models and Agent Infrastructure

Choose this path when: You are selecting technical components for an agentic application rather than choosing a ready-to-use assistant.

Key question: What additional application logic, agent orchestration, tool integration, security, and deployment services will be necessary?

After the Questionnaire

Once you have identified an appropriate path:

  1. Open its detailed comparison guide or infrastructure documentation.
  2. Confirm that the products are available in your region and account configuration.
  3. Confirm each candidate passes the privacy gate for the intended data and configuration, then verify the remaining mandatory requirements.
  4. Select two or three suitable candidates.
  5. Complete the same representative task in each environment.
  6. Record actual results, limitations, and costs.
  7. Use the Platform Comparison Scorecard to compare the candidates.
  8. Select the simplest acceptable solution.

This questionnaire is a guided decision aid, not an automated product ranking.

Return to Question 1 if your project requires a different approach.

Step 2: Choose your development approach

The questionnaire identifies the general approach. The following directory connects that approach to its detailed evaluation and learning resources.

Detailed Platform Comparisons

Approach Detailed guide What you will find
A. Use an existing agent Ready-to-Use AI Agent Comparison Individual products, supported tasks, availability, setup, hands-on tests, privacy, and operating costs
B. Build visually or automate workflows Visual and Low-Code Agent Builder Comparison Six major platforms, practical workflow construction, integration, deployment, governance, and costs
C. Use an AI coding agent AI Coding Agent Comparison Five coding environments, installation, repository development, code testing, permissions, and portability
D. Develop a custom agent Agent Development Framework Comparison Agent SDKs, orchestration frameworks, managed execution, research APIs, and a common application-development exercise
E. Choose models and infrastructure Models and Agent Infrastructure Model APIs, inference services, cloud platforms, and the technical requirements for assembling an agent system

How these articles work together:

  • The Agent Environment Selector identifies the correct approach and provides the central evaluation methodology.
  • The specialist comparison articles explain products in detail and provide practical learning exercises.
  • Official vendor documentation provides current installation instructions and product-specific technical details.
  • The Platform Comparison Scorecard records evidence and supports a documented decision.

This structure avoids maintaining multiple independent versions of product descriptions, setup instructions, or evaluation formulas.

Models and Agent Infrastructure

Models and infrastructure form the technical foundation of many custom agentic applications. Unlike the four specialist product categories, these components are not all directly comparable. A model provider, an inference service, and a managed agent platform perform different roles. The following table identifies representative offerings and directs readers to official documentation.

Offering Role in an agentic system Main evaluation considerations Official resource
Meta Muse Models Muse model capabilities accessible through the Meta Model API, including agent-oriented reasoning and tool use Model behavior, tool compatibility, context, latency, pricing, and availability Meta Model API
DeepSeek Model APIs with tool calling and support for custom agent execution loops Function calling, API compatibility, model behavior, usage cost, and reliability DeepSeek Tool Calls
Qwen Qwen models available through compatible inference and deployment options Model selection, tool-calling behavior, hosted versus local deployment, and licensing Qwen Quickstart
Alibaba Cloud Model Studio Cloud-based model access and related AI development services Provider configuration, regional services, quotas, billing, and integration Alibaba Cloud Model Studio
Groq Hosted inference and developer APIs for supported models Latency, supported models, tool calling, rate limits, throughput, and data handling Groq API Quickstart
Gemini Enterprise Agent Platform Google Cloud services for models, agent development, and related enterprise infrastructure Cloud identity, managed services, model availability, deployment, and consumption Getting Started

Important distinctions:

  • A model supplies intelligence but does not necessarily manage tools, permissions, or application state.
  • An inference service runs model requests but does not automatically implement a complete agent workflow.
  • A development framework coordinates the application's agent behavior.
  • A managed agent service may operate the execution environment, subject to the service's capabilities and terms.
  • Cloud infrastructure provides computing and supporting resources but still requires application configuration and management.

Infrastructure Evaluation Checklist

Before selecting a model or infrastructure provider, verify:

Requirement Questions to answer
Model functionality Does the model support the required reasoning, structured outputs, and tool use?
Compatibility Does it work with the selected framework, tools, and application interfaces?
Performance Are latency, throughput, and rate limits acceptable?
Context and state Can the application provide the information required for each task, and where is persistent state managed?
Data governance Does the full model, tool, hosting, storage, and monitoring configuration pass Step 0, including routing and fallback destinations?
Reliability What happens when requests fail, time out, or reach rate limits?
Scalability Can the service support expected users and execution volume?
Costs What are the charges for model usage, hosting, storage, tools, and monitoring?
Portability Can the application change providers or models without unacceptable redevelopment?

Selection principle: Choose the model and infrastructure required by the application rather than designing the application around an unnecessary collection of technical services.

For agent execution and orchestration, see Agent Development Framework Comparison.

Step 3: Compare candidate environments

Evaluate candidate environments against one clearly defined project.

Compare like with like. Ready-to-use productivity agents should generally be compared with other ready-to-use agents. Coding agents should be compared with coding agents, and agent frameworks with other appropriate development approaches.

When comparing different approaches, first determine whether both can satisfy the same project requirements. Avoid interpreting a single numerical score as proof that fundamentally different product categories are interchangeable.

Platform Comparison Scorecard

Purpose: Provide one consistent, evidence-based method for selecting among suitable agent environments.

The preferred environment is the one that meets the project's requirements with the best combination of task performance, reliability, security, cost, and maintainability.

Do not enter personal or project-specific scores directly into this public wiki page. Copy the scorecard into your own document or spreadsheet and complete the evaluation privately.

A. Check Mandatory Requirements

Before scoring, confirm a recorded Pass at the privacy gate and identify the other requirements that cannot be compromised. A failed or unverified privacy gate makes the candidate ineligible for the intended data, regardless of its potential weighted score.

Mandatory requirement What to verify
Required functionality Can the environment actually accomplish the intended task and produce the required output?
Applications and integrations Can it access the required services, files, data sources, APIs, or websites?
Data privacy eligibility Is the Step 0 gate recorded as Pass for this exact data, account, model, deployment, and connected-service configuration?
Security and action controls Can it enforce required access restrictions, credential protection, action approvals, and audit controls?
Deployment Can it operate in the required cloud, local, enterprise, or self-hosted environment?
Availability Is the required capability available in your region, account, subscription, and organizational configuration?
Essential agent behavior Does it support the required tools, scheduling, autonomy, state, or human approval?
Budget Are development, subscription, execution, and maintenance costs acceptable?
Operational responsibility Can the user or organization maintain and support the solution?

Decision: Exclude candidates with a failed mandatory requirement or an unverified privacy gate. Reconsider a changed configuration only after its workaround has been verified to satisfy the requirement and the relevant gate has been passed.

A platform should not proceed merely because it performs well in less important areas.

B. Define the Rating Scale

Rate each remaining candidate on a five-point scale:

Rating Meaning
1 — Poor Cannot adequately satisfy the requirement.
2 — Limited Has significant shortcomings or requires substantial additional work.
3 — Acceptable Meets the basic requirement, possibly with manageable workarounds.
4 — Good Meets the requirement with minor limitations.
5 — Excellent Demonstrably satisfies the requirement with minimal additional effort.
NT — Not Tested Insufficient evidence to assign a reliable rating.

Use observed results whenever possible. A capability described in promotional material is not equivalent to a successfully demonstrated capability.

C. Weighted Evaluation Criteria

The default evaluation uses eight criteria totaling 100 points.

The table below combines the criterion definitions and a blank candidate-rating worksheet to avoid maintaining duplicate scoring tables.

Copy it into a personal spreadsheet or document.

Criterion Weight What to evaluate Candidate A Candidate B Candidate C
Task functionality 20 Accuracy, completeness, required actions, and usable outputs
Integrations and data access 15 Required connectors, APIs, information sources, and integration reliability
Reliability and observability 15 Consistent execution, error handling, recovery, logs, and troubleshooting
Security, privacy controls, and permissions 15 Quality and operability of authentication, authorization, credential protection, privacy controls, approvals, and auditability among configurations that already pass mandatory requirements
Development and maintenance 10 Setup effort, technical complexity, testing, configuration, and ongoing support
Autonomy and human oversight 10 Task planning, tool selection, stopping conditions, approvals, and user control
Total cost 10 Subscription, model usage, execution, infrastructure, development, and maintenance costs
Deployment and portability 5 Hosting options, data export, model flexibility, migration, and long-term ownership
Total possible 100

Instructions:

  1. Enter the selected candidate names in your personal copy.
  2. Rate each criterion from 1 to 5.
  3. Use NT whenever the criterion has not been adequately tested.
  4. Retain evidence supporting each rating.
  5. Calculate weighted totals only when all criteria have been evaluated.
  6. Keep the default weights or deliberately adjust them for the project while preserving a total of 100.

The weights represent a useful starting point, not a universal standard.

After mandatory requirements have passed, an organization handling regulated information may give additional weight to the usability and operational assurance of security and privacy controls, while a personal productivity project may emphasize ease of use and task completion. Changing weights never relaxes the privacy gate or makes a prohibited data transfer acceptable.

D. Calculate Weighted Scores

For each criterion:

Weighted points = (Rating / 5) × Criterion Weight

Example:

A candidate receives a rating of 4 for Task Functionality, which has a weight of 20. 

(4 / 5) × 20 = 16 points

Repeat the calculation for all eight criteria.

Final weighted score = Sum of weighted points across all eight criteria

The maximum possible score is 100 points.

A total should be reported as final only when every required criterion has sufficient evidence for a rating.

Do not convert NT into zero. Doing so would incorrectly treat missing evidence as demonstrated failure.

When the evidence is incomplete, record the comparison as provisional and identify the tests still needed.

E. Record Supporting Evidence

For each candidate, retain:

  • Product name, version, interface, and subscription or service tier.
  • Date of evaluation.
  • Required applications and integrations.
  • Model and provider configuration when relevant.
  • The privacy-gate record, permitted data scope, approved destinations, terms and settings reviewed, and dates of supporting evidence.
  • Task instructions and test data.
  • Successful and unsuccessful executions.
  • Output quality and accuracy.
  • Execution logs, references, and screenshots where appropriate.
  • Manual interventions and corrections.
  • Error-handling and recovery results.
  • Permission and approval behavior.
  • Execution time and usage consumption.
  • Estimated ongoing operating costs.
  • Known limitations and workarounds.

Evidence matters more than the apparent precision of the score.

A small difference between two final scores may not be significant if the products have different untested limitations or if the measurements are uncertain.

For additional evaluation concepts, see AI Trust & Evaluation and AI Experiments & Benchmarks Navigator.

F. Interpret the Results

After completing the prototype testing in Step 4:

  1. Exclude candidates that fail mandatory requirements or have an unverified privacy gate.
  2. Review every remaining NT entry.
  3. Calculate completed weighted scores.
  4. Compare the strongest and weakest criteria.
  5. Review the practical consequences of any tradeoffs.
  6. Consider implementation and maintenance complexity.
  7. Record the reasons supporting the selection.

Do not automatically choose the highest numerical score.

The scorecard supports a decision; it does not replace professional judgment, organizational requirements, or practical testing.

Step 4: Build a representative prototype

A prototype should demonstrate that a candidate can accomplish the same real-world task under comparable conditions.

The four specialist articles contain detailed hands-on exercises appropriate to their respective environments.

Define the Test Task

Before beginning:

  1. Describe the actual problem to be solved.
  2. Specify the inputs available to the agent.
  3. Define the expected outputs and success criteria.
  4. Identify which applications or services are required.
  5. Establish permission and approval boundaries.
  6. Prepare representative synthetic or public test data and verify that the trial configuration is permitted for it. A successful synthetic-data trial does not approve use of confidential production information.
  7. Define normal, unusual, and failure conditions.

Use one consistent task across candidates wherever practical.

Follow the Appropriate Hands-On Guide

For models and inference infrastructure, test a representative application request using the intended model, tools, framework, and service configuration.

Minimum Prototype Test Cases

Test case What to demonstrate
Privacy boundary With synthetic data, verify access scope, permitted destinations, minimum necessary payloads, and applicable retention or deletion controls. Retain documentation for provider-side requirements that a prototype cannot establish.
Normal execution The system successfully completes the intended task.
Missing information It identifies missing or uncertain information rather than inventing an answer.
Unexpected input It handles unusual but valid inputs appropriately.
Tool or service failure It reports failures and recovers safely when possible.
Unauthorized action It respects permissions and stops before unapproved consequential actions.
Repeated execution It handles repeated requests without unintended duplicate effects.
Untrusted content It does not treat irrelevant instructions inside source data as authorized commands.
Operational limits Execution time, model usage, cost, and resource consumption remain acceptable.

Not every project requires every advanced test. Mandatory requirements must always be tested.

A successful demonstration is not simply an agent that produces a plausible answer. It should meet the defined success criteria and provide enough evidence to verify its actions and results.

Compare the Prototypes

For each candidate, answer:

  • Did it complete the task?
  • Was the output correct and usable?
  • How much human intervention was required?
  • Did the required integrations work?
  • Were actions logged and reviewable?
  • Did permission boundaries hold?
  • How did it handle failures?
  • How much did execution cost?
  • Can the solution be maintained?

Record those findings in your personal copy of the Platform Comparison Scorecard.

Step 5: Validate and deploy

A successful prototype identifies a promising solution. It does not automatically establish that the solution is ready for ongoing or production use.

Before deployment, validate the operating environment, permissions, reliability, cost, and maintenance plan.

Deployment Readiness Checklist

Area Validation question
Functionality Does the solution consistently meet its documented requirements?
Security Are access rights, credentials, tools, and approval controls configured appropriately?
Privacy Does the production configuration still pass Step 0 for its actual data, users, destinations, retention, and applicable organizational requirements? Retain the evidence and required approval before rollout.
Reliability Are errors, retries, interruptions, and recovery adequately handled?
Observability Can the operator inspect executions, failures, and important actions?
Usage and costs Are limits, consumption, and expected operating costs understood?
Maintenance Is someone responsible for changes, upgrades, and troubleshooting?
Portability Can essential information, source code, instructions, or configurations be retained or migrated?
Human oversight Can users review, pause, stop, or revoke consequential activities?

For higher-risk applications, apply additional reviews, controls, and domain-specific testing as appropriate.

Record the Final Decision

Decision item Record in your project documentation
Project Name, objective, and intended outcome
Selected environment Product, version, edition, and configuration
Development approach Existing agent, visual builder, coding agent, framework, or infrastructure
Alternatives evaluated Other candidates tested
Mandatory requirements Conditions verified, privacy-gate status, and workarounds demonstrated to satisfy the requirements
Evaluation results Final score, supporting evidence, and important findings
Costs Expected subscription, execution, development, and maintenance expenses
Security and governance Permissions, approvals, data handling, and operational controls
Limitations Known weaknesses, restrictions, and unresolved risks
Maintenance responsibility Person or team responsible for operation and support
Next action Pilot, deployment, additional testing, or reconsideration
Re-evaluation date Date to reconsider the decision as requirements or products change

Final selection principle:

Choose the simplest environment that reliably satisfies the project's requirements within acceptable limits for cost, security, reliability, and maintenance.

Do not introduce custom development, multi-agent orchestration, or complex infrastructure unless the task and supporting evidence justify that additional work.

Continuing Your Learning

After selecting an environment:

  1. Complete its official beginner tutorial.
  2. Build one useful task or small project.
  3. Test it with realistic examples.
  4. Document what worked and what did not.
  5. Explore additional capabilities only when needed.
  6. Revisit the selection when project requirements materially change.

The specialist comparison articles provide the detailed starting points for each of the four principal agent-environment approaches.

Related PRIMO.ai Resources

Agent environment selection:

Agent design and AI development:

Evaluation and responsible operation:

Where next? ◀ Generate & converse · ...answer questions from my data · ...choose a model · ...deploy AI · ▶ ...evaluate and trust AI
Retrieved from "https://primo.ai/w/index.php?title=...build_an_agent&oldid=52703"