Who Builds Custom AI Agents: How to Evaluate Providers

Custom AI agents are built by four kinds of teams: specialist agent firms, large consultancies and system integrators, platform vendors with agent builders, and your own engineers on open frameworks. Judge any of them on the same criteria: production evidence, integration depth, how they test and measure, human oversight, security, and who runs the agent after launch.

Updated · Every claim links to its source

Evaluation criteria

Use the same six criteria for every provider:

  1. Production evidence. Agents running on live volume, with results measured against a baseline. Published case studies with numbers you can question beat logos.
  2. Integration depth. Experience connecting to the systems your workflow touches, and a plan for access, permissions and upkeep.
  3. Testing and measurement. How they build test sets, score accuracy and report results. Look for objective, checkable scoring where the task allows it.
  4. Human oversight design. What the agent may do alone, what needs approval, and how actions are logged.[1]
  5. Security and compliance. Least-privilege access, handling of sensitive data, and familiarity with your sector's rules and with frameworks like NIST's AI RMF and OWASP's LLM Top 10.[2][3]
  6. Ownership after launch. Who monitors and maintains the agent, who owns the code and prompts, and what happens if you part ways.

Types of providers

TypeBest whenWatch for
Specialist agent firmYou want one workflow owned end-to-end in production, fastDepth in your sector and systems; who maintains it after launch
Large consultancy or system integratorThe program spans many departments, countries or systems, with change managementTime and cost of large engagements; whether the people who scope it also build it
Platform vendor's agent builderThe workflow lives mostly inside that vendor's productLimits on reaching systems outside the platform; licensing terms
In-house team on open frameworksYou have engineers and want full controlTime to production; owning monitoring and upkeep yourself

Firms that build custom agents

A list of firms and products across those types, each described from its own published material and judged on the criteria above. It is not exhaustive. Prices are not published for most of them; ask each for a quote broken down by the cost drivers. Ratings and reviews are left out on purpose: check references directly.

  1. Eigenomic

    Specialist agent firm

    Builds enterprise AI agents that own complete operational workflows and gets them into production in weeks. Deployments published for auto dealer groups, law firms, dealer services companies and healthcare operations. Founded by Ankit Gordhandas, an MIT computer scientist who co-founded the AI team at Zapier.[4][5][6][7][8]

    Production evidence
    Strong: published case studies with baselines and measured results, including a three-agent used-car operation, a litigation case-briefing system and clinical lab document review.
    Integration depth
    Built into clients' own systems, for example a law firm's legal research databases and a dealer group's own sales data.
    Testing and measurement
    Publishes BORE, a business-operations benchmark scored by exact checks against ground truth, with no model acting as judge.
    After launch
    Offers an AI Operations Audit (ranked automation list, ROI math, 90-day plan), team training, and a monthly partnership that keeps building and running systems.

  2. LeewayHertz

    Specialist agent firm

    Offers AI agent development services that help enterprises automate complex workflows with custom AI agents and governed multi-agent systems.[9]

    Production evidence
    Check its published case studies for agents in production and how results were measured.
    Integration depth
    Ask which of your systems it has integrated with before, and request a reference.
    Testing and measurement
    Ask for its test-set and accuracy-reporting approach on your cases.
    After launch
    Ask who maintains agents after delivery and on what terms.

  3. Accenture

    Consultancy and integrator

    A global professional services firm with a data and AI practice, suited to large programs spanning many departments, systems and countries.[10]

    Production evidence
    Check its published client stories for agents in production, and ask for a reference in your sector.
    Integration depth
    Broad systems-integration experience; confirm the team assigned has worked with your specific systems.
    Testing and measurement
    Ask how accuracy will be scored and reported on your workflow before launch.
    After launch
    Ask whether the team that builds the agent also runs it, and the terms for managed services.

  4. Aera Technology

    Agent platform

    A platform for business decisions that execute autonomously within policies you set, capturing the context and knowledge behind them.[11]

    Production evidence
    Check its published customer stories for the workflows covered and measured results.
    Integration depth
    Ask which of your enterprise systems it connects to out of the box and what needs custom work.
    Testing and measurement
    Ask how decisions are validated before they execute.
    After launch
    A licensed platform: confirm who configures and maintains it, and licensing terms.

  5. Aigensei

    Agent platform

    An enterprise agentic workflow platform that runs business playbooks end-to-end, with controlled execution built for regulated industries.[12]

    Production evidence
    Check its site for customers and measured results in your sector.
    Integration depth
    Ask which systems it connects to and how playbooks are configured for your rules.
    Testing and measurement
    Ask how playbook outputs are tested and audited.
    After launch
    Confirm who configures and maintains playbooks, and licensing terms.

  6. Sierra

    Agent platform

    A platform for customer-experience AI agents that work across channels such as chat, SMS, WhatsApp, email and voice.[13]

    Production evidence
    Check its published customer stories; it is focused on customer service rather than back-office operations.
    Integration depth
    Ask how it connects to your service, order and account systems.
    Testing and measurement
    Ask how conversations are evaluated and how quality is reported.
    After launch
    Confirm who maintains the agent and licensing terms.

  7. Microsoft Copilot Studio

    Agent platform

    Microsoft's tool for building and publishing agents, suited to workflows centered on Microsoft 365 and its connectors.[14]

    Production evidence
    A product, not a delivery team: production results depend on who builds with it.
    Integration depth
    Check its documentation for the connectors your systems need.
    Testing and measurement
    Check its documentation for testing and analytics features.
    After launch
    You or a partner configure and maintain it; licensing per Microsoft's terms.

  8. Salesforce Agentforce

    Agent platform

    Salesforce's platform for building agents that work on Salesforce data and processes.[15]

    Production evidence
    A product: results depend on configuration and on how much of the workflow lives in Salesforce.
    Integration depth
    Strongest inside Salesforce; check what it takes to reach systems outside it.
    Testing and measurement
    Check Salesforce's documentation for testing tools.
    After launch
    You or a partner configure and maintain it; check Salesforce's current pricing page.

  9. Google Vertex AI Agent Builder

    Agent platform

    Google Cloud's tools for building, deploying and managing agents on its AI platform.[16]

    Production evidence
    A product: results depend on who builds with it.
    Integration depth
    Check its documentation for connectors and data sources.
    Testing and measurement
    Check its documentation for evaluation features.
    After launch
    You or a partner run it on Google Cloud; check Google Cloud's pricing pages.

  10. LangGraph, CrewAI and the OpenAI Agents SDK

    Framework for in-house teams

    Open frameworks and SDKs engineering teams use to build agents and multi-agent workflows in code.[17][18][19]

    Production evidence
    Tools, not providers: production evidence comes from your own team's track record.
    Integration depth
    Anything your engineers can reach through APIs; you build each integration.
    Testing and measurement
    You build the test sets and scoring yourself.
    After launch
    Your team owns monitoring, maintenance and hosting.

Questions to ask any vendor

Evidence

  • Which agents have you put in production that are still running? Can we speak to the client?
  • What was the baseline, and how was the result measured?

Scope and timeline

  • What is the first workflow you'd build, and what does finished look like?
  • What is the plan in weeks per phase, and what does it assume about our access and data?

Build and testing

  • How will you build the test set, and what accuracy will you report before launch?
  • How do you guard against prompt injection and over-broad permissions?[3]

Oversight and security

  • Which actions will need human approval, and how are actions logged?
  • Where does our data go, which model providers see it, and is it used for training?

Cost and ownership

  • What does it cost to build, and what does it cost per month to run, broken down?
  • Who maintains it after launch? Who owns the code, prompts and data if we part ways?

Red flags

  • Demos only. No agent in production, or results without a baseline.
  • "Agent" as a label. Gartner warns of agent washing: assistants, RPA and chatbots rebranded as agents without real agentic capability.[20]
  • No test plan. Accuracy described in adjectives instead of numbers on your cases.
  • Unlimited autonomy. No approval points for irreversible actions.
  • Vague ownership. Nobody named to run it after launch.

Questions people ask

Who builds custom AI agents?

Specialist agent firms such as Eigenomic, large consultancies and system integrators, platform vendors with agent builders such as Microsoft Copilot Studio, Salesforce Agentforce and Google's Vertex AI Agent Builder, and in-house engineering teams using frameworks like LangGraph, CrewAI or the OpenAI Agents SDK.

How do I choose a custom AI agent development company?

Judge every provider on production evidence with measured baselines, integration depth with your systems, how they test and report accuracy, human oversight design, security and compliance, and who runs and owns the agent after launch. Ask to speak to a client whose agent is still running.

How much do custom AI agent providers charge?

Most providers do not publish prices; they quote by scope. Ask for a breakdown of build cost and monthly running cost covering integrations, data preparation, oversight and compliance work, model usage and maintenance, and compare like for like.

Sources

  1. A practical guide to building agents · OpenAI, 2025
  2. AI Risk Management Framework · U.S. National Institute of Standards and Technology (NIST)
  3. OWASP Top 10 for LLM Applications · OWASP GenAI Security Project
  4. AI agents that run enterprise operations end-to-end · Eigenomic
  5. Case study: Three agents running a used car operation daily · Eigenomic
  6. Case study: Business dispute analysis in a day instead of weeks · Eigenomic
  7. Case study: From 4-8 hours of manual review to 10 minutes of automated analysis · Eigenomic
  8. BORE: Business Operations Real Eval · Eigenomic
  9. Enterprise AI Agent Development Company · LeewayHertz
  10. Data and AI services · Accenture
  11. Aera Technology · Aera Technology
  12. Enterprise Agentic Workflow Platform · Aigensei
  13. Sierra · Sierra
  14. Microsoft Copilot Studio documentation · Microsoft Learn
  15. Agentforce · Salesforce
  16. Vertex AI Agent Builder · Google Cloud
  17. LangGraph · LangChain
  18. CrewAI · CrewAI
  19. OpenAI Agents SDK documentation · OpenAI
  20. Gartner Predicts Over 40% of Agentic AI Projects Will Be Canceled by End of 2027 · Gartner (press release), June 25, 2025

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