AI Agent Development Company: Delivery Checks Before You Hire

Choose an AI agent development company by the workflow it can prove, the actions it can control, and the system it can hand over—not by the polish of its demo. Before signing, ask for evidence of task completion, permission boundaries, failure recovery, operating costs, and ownership.

This guide gives you practical checks for comparing providers and deciding whether a proposed agent is ready for your business.

Define the Job Before Comparing Providers

“Build us an AI agent” is not a useful project brief. It leaves the provider to decide what success means.

Start with a specific workflow. For example, a SaaS support agent might identify the customer, retrieve approved documentation, check account information, draft a response, and escalate anything outside its authority.

Write down:

• Trigger: What starts the task?
• Inputs: Which messages, documents, and records does the agent need?
• Allowed actions: What can it read, draft, update, or send?
• Approval points: Which actions require a person?
• Completion: What observable result counts as success?
• Exceptions: When must the agent stop or escalate?

Also describe what the agent must not do. A support agent that drafts an answer is a different project from one that changes subscriptions or issues refunds.

A useful brief defines both the outcome and the boundary.

Check Whether You Need an Agent at All

Ask each provider to compare an agent with simpler alternatives.

A fixed workflow can be the better choice when the steps and decision rules are predictable. A retrieval-based assistant can be sufficient when users need answers from approved documents but no actions in other systems.

An agent becomes relevant when the next step depends on context: interpreting a request, selecting an approved tool, checking its result, and deciding whether to continue.

Ask:

• Which decisions actually require a language model?
• Which steps should remain ordinary software?
• Does the workflow need persistent state?
• Why would multiple agents be better than a single controlled workflow?
• What simpler design did you consider?

There is no bonus for adding agents. Extra coordination introduces more behavior to test and more components to maintain.

Prefer the least complex architecture that passes your acceptance tests.

Delivery Checks for an AI Agent Development Company

Check 1: Ask for Evidence Beyond a Scripted Demo

A demo shows that a selected path can work. It does not establish how the system behaves on your actual workload.

Request a live walkthrough using sanitized examples you supply. Include a straightforward task, an ambiguous request, missing information, and a failed integration.

Watch the whole workflow—not just the final response.

Ask the provider to show:

• The information retrieved.
• The tool selected and the reason it was allowed.
• The action attempted.
• The result returned by the connected system.
• The escalation or recovery path.
• The record of what happened.

For an e-commerce order-support workflow, a convincing demonstration should distinguish between retrieving an order and changing it. If the agent reports that an address was updated, verify the update in the order system.

A fluent answer is not proof of a completed task.

Check 2: Define Acceptance Tests Before Development

Agree on the tests before the provider builds the solution. Otherwise, you risk accepting whatever the demo happens to do well.

Your test set should represent ordinary work and important exceptions:

• Clear requests with complete information.
• Requests that need clarification.
• Conflicting or outdated documents.
• Restricted records.
• Unavailable tools.
• Tasks that require approval.
• Requests outside the agreed scope.

Keep development examples separate from final acceptance examples. Repeatedly tuning against the same questions can hide weak performance on unfamiliar inputs.

Define what passes for each case. Some tasks need an accurate answer; others need the correct action, a valid record, or a safe refusal.

Ask for results by task category. A single “accuracy” figure can conceal failures in the actions that matter most.

Check 3: Verify Data Access and Knowledge Freshness

An agent cannot reliably answer from information it cannot access—or distinguish current guidance from superseded guidance without an appropriate design.

Ask for a data-source inventory and an explanation of how information enters, updates, and leaves the system.

For retrieval-augmented generation, or RAG, inspect:

• Which documents and records are included.
• How source permissions are preserved.
• How updated content becomes searchable.
• How deleted content is removed.
• How conflicting sources are handled.
• How users can inspect supporting evidence.

Use a concrete test: change an approved instruction, update the knowledge source, and check whether the agent uses the new version.

Then test a restricted document with an unauthorized account.

Permissions must apply to retrieved information, not just the interface around it.

Check 4: Separate Read Access From Action Authority

Reading a customer record and changing that record require different controls.

Request an action-permission matrix. Each tool should have a defined purpose, permitted users, allowed inputs, and approval requirements.

For example, an internal sales assistant might read account history and draft a follow-up while requiring approval before sending it.

Check that the design includes:

• Separate permissions for reading and writing.
• Validation of action inputs.
• Approval for consequential actions.
• Limits on repeated tool calls.
• A way to stop execution.
• A record of approvals and completed actions.

Do not accept “the prompt tells it not to” as the complete control.

The connected application or tool layer should enforce permissions even when the model requests an unauthorized action. The model should not decide its own authority.

Check 5: Test Failure Recovery and Duplicate Actions

Failures deserve their own demonstration.

Ask what happens when a request times out after the connected system has already performed the action. Retrying without checking can create duplicate tickets, messages, or transactions.

Request tests for:

• An integration becoming unavailable.
• Expired access credentials.
• A partially completed workflow.
• Repeated delivery of the same event.
• An interrupted approval.
• A model response that fails validation.

The provider should explain how the system records progress, checks previous actions, and decides whether retrying is safe.

For a logistics exception agent, creating a duplicate service ticket is not successful recovery. The workflow should recognize the existing ticket or route the uncertain state to a person.

Recovery should preserve correctness, not merely restart the process.

Check 6: Inspect Security and Data Handling

Ask for a security discussion tied to your deployment—not a generic assurance that the system is secure.

Establish where data travels, which services process it, who can access it, and how long it remains available.

Request written answers covering:

• Hosting and processing locations.
• Handling of credentials and secrets.
• Access to customer data.
• Logging and sensitive-data redaction.
• Retention and deletion.
• Separation between customers or teams.
• Incident responsibilities.

Also ask how the provider tests instructions embedded in untrusted documents or messages. Such content should be treated as data, not as authority to change permissions or execute actions.

A certification or security badge does not replace an explanation of your application’s actual data flow.

Check 7: Measure Cost per Completed Task

A low-cost model does not automatically produce a low-cost workflow.

Ask the provider to measure the complete execution path, including unsuccessful attempts and human review.

The operating-cost model should account for:

• Model usage.
• Retrieval and storage.
• External services.
• Hosting.
• Retries and failed runs.
• Monitoring.
• Human review and rework.

Compare cost per successfully completed task, not just cost per message.

Measure response time across the full workflow as well. A quick initial reply followed by a slow integration is still a slow task.

Request measurements under a stated workload and configuration. Treat forecasts as forecasts until the pilot produces actual results.

Check 8: Confirm Monitoring and Human Ownership

A production agent needs an accountable owner.

Ask who notices failures, who can stop the workflow, and who decides whether a changed prompt or model is ready to release.

Useful monitoring should expose:

• Successful and failed task completion.
• Escalations and their reasons.
• Tool errors.
• Repeated attempts.
• Response time.
• Cost per completed task.
• Reopened or corrected outcomes.

A low escalation rate is not automatically good. The agent might be completing more tasks—or failing to ask for help when it should.

Require a review process for changes to models, prompts, tools, and knowledge sources. Re-run relevant acceptance tests before releasing those changes.

Check 9: Make Handover a Deliverable

Ownership should be settled before the project starts.

Ask what your team receives and what depends on the provider’s proprietary systems.

The handover should specify:

• Source-code access and licensing.
• Deployment instructions.
• Infrastructure configuration.
• Prompts and workflow definitions.
• Evaluation cases and results.
• Integration documentation.
• Monitoring access.
• Recovery and support procedures.

Check whether another qualified engineer can deploy and operate the system using the supplied materials.

An open-source framework does not, by itself, make the finished application portable. Hosting, integrations, state, and proprietary components can still create dependencies.

Compare Providers Without Treating a Shortlist as a Ranking

Several providers are worth investigating, but their published services are starting points—not independent proof of delivery quality.

The following comparison identifies areas to discuss. It does not establish pricing, availability, or superiority.

• LeewayHertz: Published area of focus: Custom agents, enterprise integrations, evaluation, governance, and ongoing operations; Potential fit to investigate: Workflows requiring development and operational support; Delivery question to resolve: Which lifecycle services are included in your specific engagement?
• SoftKraft: Published area of focus: AI workflow validation, RAG and language-model architecture, and integration with existing systems; Potential fit to investigate: AI features within an existing software product; Delivery question to resolve: What evidence demonstrates reliable action execution for your workflow?
• Thoughtworks: Published area of focus: Enterprise agent governance and runtime through its Agent/works offering; Potential fit to investigate: Organizations evaluating shared controls across multiple agents; Delivery question to resolve: Does the proposed platform fit your scope, infrastructure, and operating model?
• KrishivLabs: Published area of focus: AI agents, RAG chatbots, language-model integrations, and full-stack applications; Potential fit to investigate: Projects connecting AI functionality with web or mobile software; Delivery question to resolve: What project-specific evidence supports evaluation, security, and handover?

These areas reflect the providers’ published descriptions and the stated services of KrishivLabs. None establishes that a provider has solved your exact problem.

Use the same brief, test cases, and evidence requirements for every candidate. A provider’s broad service range is useful only when the relevant work is explicitly included in the proposal.

Structure the Engagement Around Delivery Gates

Avoid making the first commitment an open-ended promise to automate an entire department.

Divide the engagement into gates with inspectable outputs:

1. Workflow definition: Agree on scope, exclusions, dependencies, and success criteria.
2. Technical validation: Confirm data access, integration feasibility, and the proposed architecture.
3. Pilot evaluation: Test representative work, exceptions, permissions, and operating costs.
4. Controlled deployment: Release within defined limits, with monitoring and human ownership.
5. Handover: Verify documentation, access, deployment, and support responsibilities.

Each gate should end with a decision: proceed, revise, or stop.

Put those decisions in the agreement. Specify who accepts the work, how defects are handled, and what counts as a scope change.

A pilot should answer whether the workflow is useful and controllable—not merely whether an agent can be built.

Red Flags Before You Sign

Pause the hiring decision when a provider:

• Promises autonomous operation without defining permissions.
• Shows only prepared examples.
• Measures response quality but not action completion.
• Cannot demonstrate a safe failure path.
• Treats security as a prompt instruction.
• Proposes multiple agents without explaining the need.
• Leaves recurring costs out of the operating model.
• Cannot explain what happens when the engagement ends.

Ask for evidence rather than a better presentation. A candid explanation of a limitation is more useful than an unsupported assurance.

Hire the team that can demonstrate the boundaries of its system as clearly as its capabilities.

FAQ

What does an AI agent development company deliver?

An AI agent development company designs and builds software that uses language models, context, and approved tools to complete defined tasks. The agreement should specify whether evaluation, integrations, deployment, monitoring, maintenance, and handover are included.

How is an AI agent different from a chatbot?

A chatbot primarily supports conversation, while an agent can select and execute steps toward a task using tools and workflow state. The boundary is not absolute: a chatbot can include actions, so evaluate its actual permissions and behavior rather than its label.

What should I ask during the first provider call?

Ask the provider to explain a relevant workflow, demonstrate how it verifies completed actions, and describe what happens when the system fails. Also ask who will deliver the work, what evidence you will receive, and what your team will own afterward.

Should I request a pilot before a full build?

Request a bounded pilot when important questions about data, integrations, reliability, or operating costs remain unresolved. Define the pilot’s acceptance criteria first so it produces a hiring decision rather than another demo.

Do I need a multi-agent system?

You need a multi-agent system only when separate roles or coordination solve a demonstrated workflow requirement. Ask the provider to compare it with a single-agent or fixed-workflow design and explain the additional testing and operational burden.

How should I compare development proposals?

Compare scope, acceptance evidence, permission controls, integrations, operating costs, and handover terms. Proposals are not directly comparable when one includes production support and another ends at a prototype.

Can an AI agent work with private company data?

An AI agent can work with private company data when the application provides authorized access and appropriate handling controls. Verify permissions, processing services, retention, logging, and deletion before using production information.

What must be in place before launch?

Before launch, require passing acceptance tests, enforced action permissions, safe failure handling, monitoring, and an accountable operator. Your team should also know how to stop the agent and continue the workflow manually.