Key takeaways
- Compare operating work, not just the builder: A visual interface can simplify configuration without taking over connector maintenance or production support.
- Start with the workflow: Source changes, permissions, unsupported questions, and tool actions matter as much as a successful demo answer.
- Keep architecture choices separate: Authoring style, hosting location, and who operates the system are independent decisions.
- Evaluate operational split: Determine whether developers can establish the required controls while content owners can manage routine review and configuration tasks.
An enterprise document assistant needs more than a chat box. Someone must connect sources, maintain access rules, check retrieval quality, and handle failures after documents or APIs change.
When evaluating architectures, distinguish between two distinct axes: authoring style and operating model. No-code, visual, or configuration-first authoring is an authoring style that may exist within managed cloud services or integrated platforms; it is not a standalone, mutually exclusive operating model. Furthermore, visual builders vary in technical capability and are not all fully no-code. The three primary operating models—code-first frameworks, managed cloud services, and integrated platforms—are also not rigid silos; teams often combine elements, such as running a code-first framework inside a managed container service or connecting custom code to an integrated platform via APIs.
Historically, data preparation has required substantial operational effort. For context on data processing workload overhead, in Anaconda’s 2020 State of Data Science survey (2,360 respondents across more than 100 countries), respondents reported spending an average of 45% of their time loading and cleaning data. While this historical, self-reported data-science metric does not measure modern Retrieval-Augmented Generation (RAG) implementation efforts, budget shares, or guaranteed platform savings, it illustrates why evaluation pilots should measure document preparation and ingestion labor.
This comparison examines how code-first frameworks, managed cloud services, and integrated platforms allocate development and operational responsibilities.
Start with a code-first framework when control is essential
A framework gives developers a way to express the assistant's flow in code. That is suitable when a workflow has custom routing, specialized tools, or approval steps that a packaged interface cannot represent clearly.
LangGraph provides low-level orchestration for stateful, long-running agents. It supports patterns involving persistence and human intervention and can be used independently of LangChain. Those capabilities describe the orchestration layer; they do not, by themselves, supply a complete enterprise document service.
The team still needs to select and integrate source ingestion, parsing, retrieval, identity management, deployment, and monitoring components. This approach fits teams where those components already exist and developers require fine-grained control over how they interact. Include the work of maintaining those underlying services in the evaluation rather than treating a working local example as a finished operating model.
Consider managed cloud services when the team wants a supported runtime
Managed services can reduce infrastructure management while leaving application logic and integration choices with the customer. The operational balance varies by service and deployment mode.
Microsoft Foundry Agent Service illustrates this spectrum by distinguishing between Prompt agents and Hosted agents:
- Prompt agents are defined through configuration (such as instructions, model selection, and tools) via portal, SDK, or REST endpoints, leaving underlying execution and scaling to the managed platform.
- Hosted agents allow teams to bring custom code and frameworks, which the service runs in managed containers with integrated scaling, endpoints, and identity.
Configuring an agent through a portal or API does not imply an absence of programmatic authoring, nor should identical feature availability or regional support be assumed across different modes. Evaluate the complete assembly: identify which service handles the source index, how the agent authenticates to internal tools, where operational traces are reviewed, and what feature capabilities exist in your deployment region.
Evaluate an integrated platform for repeated connection and operations work
An integrated platform can fit teams seeking to eliminate repetitive integration across document processing, storage, retrieval, and agent orchestration. Suitability depends on whether the platform supports the specific sources, document formats, and operational workflows required.
Seahorse Cloud combines object storage, document parsing, vector retrieval, and managed agents within a unified platform. This supported scope can reduce manual integration between individual retrieval components. Teams should use a pilot to identify any remaining custom connections and define long-term operating responsibilities.
Visual or configuration-based interfaces within a platform do not eliminate technical management. Teams should clarify which routine adjustments content owners can perform independently, which tasks require developer intervention, and how changes move through review to production. Additionally, distinguish between exposing the assistant to client applications via an API and consuming tools via Model Context Protocol (MCP); these serve distinct architectural roles.
Use one acceptance worksheet across all approaches
To evaluate overall efficacy, pilots should measure task outcomes alongside system performance. In a study of workplace AI adoption, Brynjolfsson, Li, and Raymond (“Generative AI at Work,” Quarterly Journal of Economics, 140(2), May 2025) studied 5,172 customer-support agents and found a 15% average increase in issues resolved per hour. This research quantifies human workflow productivity in a specific enterprise support setting; it does not measure document parser throughput, system response speeds, or an inherent superiority of visual platforms over code frameworks. It demonstrates why evaluation pilots should measure verified task completion alongside system latency.
Run identical test workloads across each candidate architecture. The table below outlines proposed pilot measurements and operational tests rather than reported product benchmarks.
TABLE / Scroll horizontally for all columns →
| Test | Evidence to collect | Decision it supports |
|---|---|---|
| Add and update a source document | Processing status, retrieved version, ingestion-to-searchable latency, and compute hours | Whether ingestion pipelines meet freshness requirements |
| Revoke a user's source access | Search results, generated answer, and cached-result behavior across access levels | Whether configured permission rules are enforced |
| Ask an unsupported question | Response text, citation accuracy checks, and hallucination handling | Whether missing evidence is handled without incorrect claims |
| Fail an external tool call | Error handling, retry behavior, and operator trace availability | Whether failures can be isolated and remediated safely |
| Change a prompt or workflow | Review flow, version control history, and rollback execution time | Whether routine updates can be managed safely |
| Move to a second interface (e.g., a web app, Teams, Slack, or another client) | Identity context, permission enforcement, conversation/state continuity, and backend service reuse | Whether sample client test destinations can reuse the core backend without duplicating logic (this tests architecture reuse, not native product connectors) |
| Execute concurrent query workload | p50 and p95 end-to-end latency at stated query concurrency, supported-answer rate, and cost per accepted answer (calculated as total workload cost divided by verified correct answers) | Whether response speeds and operating costs remain acceptable under load |
Assign each test to the role expected to maintain it in production. A developer-managed demonstration does not establish that content owners can perform routine review or configuration tasks independently. Record setup time, maintenance effort, and error recovery steps for every candidate.
The listed clients are example test destinations; confirm native connector availability separately. An accepted answer must meet pre-agreed correctness, citation, and permission criteria. Cost per accepted answer equals total measured query-workload cost, including failed and rejected attempts, divided by the number of accepted answers.
Make the decision from responsibilities and total cost of ownership
To evaluate financial fit accurately, teams should calculate total cost of ownership (TCO) based on their specific workload requirements. Evaluate candidates using an identical document corpus, query volume, permission model, and response quality standard.
A 12-month TCO evaluation should separate one-time implementation expenses from recurring operational costs while avoiding double-counting components included in bundled subscriptions.
12-month TCO = one-time implementation cost + recurring costs over 12 months. A loaded hourly rate includes compensation, benefits, and allocated employment overhead.
One-time costs: initial engineering and integration hours × loaded hourly rate, plus initial ingestion and indexing charges not already counted.
Recurring costs: annual maintenance and support hours × loaded hourly rate, plus 12 months of software/platform fees, infrastructure/storage, model/tool usage, and ongoing ingestion or reprocessing charges. Count each expense once: exclude bundled items and any ingestion/reprocessing labor or usage already recorded in another category.
These are illustrative responsibility patterns, not fixed category rules. A framework can use managed infrastructure. For each proposal, assign hosting, upgrades, backups, and incident response explicitly to the customer or provider under the chosen deployment and service agreement.
Operating responsibilities across approaches
TABLE / Scroll horizontally for all columns →
| Approach | Who builds and configures | Who operates and maintains | Typical fit criteria |
|---|---|---|---|
| Code-First Framework | Developers typically write orchestration, tool integration, and ingestion pipelines | Customer engineering teams usually manage infrastructure, dependency updates, and operational monitoring | Workflows requiring fine-grained custom logic, specialized routing, or direct integration with existing microservices |
| Managed Cloud Service | Developers or technical authors configure agents (via portal, SDK, or REST) or deploy custom hosted code | Cloud provider manages underlying runtime host and infrastructure scaling; customer typically maintains application logic, data sources, and tool endpoints | Teams operating within an established cloud ecosystem seeking managed scaling and identity integration |
| Integrated Platform | Authors and developers use unified platform tools for parsing, storage, and agent setup | Platform vendor manages core component integration; customer typically manages content governance, prompt rules, and tool connections | Teams looking to standardize document ingestion, vector search, and agent execution within a pre-integrated stack |
Keep hosting location separate from commercial and operational responsibilities. Software deployed on customer-controlled infrastructure may still include vendor operational support under a service agreement, whereas hosted cloud services may leave application maintenance and source connector health with the customer. Choose the approach whose measured pilot performance, operational boundary, and 12-month TCO align with team capabilities.
FAQ
Does visual or configuration-first authoring mean developers are unnecessary?
No. While configuration-first authoring can reduce routine instruction updates, software engineers are still required to manage identity integration, custom data connectors, external tools, deployment controls, and security testing.
Is a code-first framework always cheaper than a managed service or integrated platform?
No. Frameworks can reduce upfront software licensing fees, but may increase ongoing engineering maintenance, infrastructure provisioning, and operational overhead depending on team capabilities and deployment architecture. Compare total cost of ownership for identical document corpora, query volumes, and support requirements.
Can a team combine visual authoring with custom code?
Yes, where the selected product provides the required extension mechanism. Verify how custom code connects to visual authoring, how it is deployed, and how changes are reviewed. Separate hosted-code modes or API availability alone do not establish round-trip visual/code editing or arbitrary code execution inside a visual flow.