Compare AI Agent Architecture Build Types

Workflows vs agents: the line that matters

Anthropic draws the line this way. "Workflows are systems where LLMs and tools are orchestrated through predefined code paths." Agents "are systems where LLMs dynamically direct their own processes and tool usage." For well-defined tasks, the same guide says, "workflows offer predictability and consistency," while agentic systems "often trade latency and cost for better task performance" (Anthropic, Building effective agents). LangGraph's documentation uses the same split: workflows "have predetermined code paths," agents "define their own processes and tool usage."

The cloud architecture guides agree on where to start. Microsoft's orchestration guide says to "use the lowest level of complexity that reliably meets your requirements" (Azure Architecture Center). Google Cloud writes, "we recommend that you start with a single agent" (Choose a design pattern for your agentic AI system).

The five build types on this page sit at different points on that line. A deterministic workflow with LLM calls sits at the workflow end; a ReAct loop and a model-directed multi-agent framework sit at the agent end; an SDK build, an RPA flow and a code-defined multi-agent graph sit in between depending on how they are configured. "Deterministic workflow" here means LLM steps inside code-defined control flow. It does not mean a BPM engine. The money side of the decision lives on the build vs buy cost and TCO page.

One label on the table needs a note. A fixed prompt chain, where "each LLM call processes the output of the previous one" along a code-defined sequence, is a workflow under both definitions above, and this page counts it with the deterministic build type. The ROI calculator labels one preset "ReAct / sequential prompt-chain agent"; that pairing is a calculator modeling convention for loops where the model directs each step and the context grows as it runs, and the ReAct row below describes that loop.

Five agent architecture build types compared

Planning presets come from the ROI calculator's planning assumptions and are editable assumptions, not measurements. The "policy" column describes the usual implementation of each build type; a team can add a separately governed policy layer to any of them and then owns building and running it.

What every build has to add for regulated document work

The architecture decides how the model is called. Whether a loan file, a claim or a draw request is processed correctly under policy depends on the layers around it, and every one of the five patterns needs the same ones.

• A document pipeline: ingestion, classification, splitting, extraction and validation across inconsistent packages.
• A policy layer, with versioning, review and rollback when the credit or claims policy changes, whether the rules live in code, in configuration or in a separate policy service.
• Evals that run over repeated runs and again at every model upgrade.
• Review queues for the cases the checks cannot settle.
• Decision evidence that ties each outcome to its source pages and the policy version in force.
• Capacity, retries and on-call.

The hub lists the eight layers a build has to add in detail, with the engineering each one takes.

How MightyBot differs

MightyBot is an AI agent platform for regulated document workflows. Your policy owners write the rules as plain-English policies. The Agent Compiler turns that description into the schemas, the workflow and a versioned execution plan. The description is the source, and the compiled plan is what runs in production.

Execution is hybrid. Deterministic checks and LLM steps run inside one compiled agent execution plan, and the platform picks the execution style per task: a compiled plan for structured work, stepwise reasoning where judgment is needed, planned sequences for long-running work.

Documents come in through the Document Intelligence Pipeline, which classifies, splits, extracts, normalizes, reconciles and recomposes each package and attaches document intelligence with page-level evidence pointers down to the character.

Policy Profiles let one workflow vary its rules by jurisdiction, counterparty or product; every decision records the profile in effect. Each decision also produces a why-trail: policy version, values checked, evidence pointers, evaluation results and the final determination, with compliance exports. The platform is model-neutral; the question is which execution style the task needs.

Proof, attributed to its deployment: 99% or higher accuracy was observed in one construction draw review deployment; validate other workflows on your own data. See production results.

When building makes sense

The hub's test holds here. Build when agent infrastructure is your core product IP, when the workflow is experimental or low-risk, or when your team is ready to own every layer above indefinitely, including evals, model upgrades, security and rollback. Run the numbers on the build vs buy cost and TCO page before you decide.

Each pattern has a case where it is a fine choice.

• A deterministic workflow plus LLM calls suits a well-defined task that splits into verifiable steps and a platform team that wants to own it.
• A self-hosted vendor agent SDK suits a team standardized on one model provider that wants a shorter build for tasks that fit the SDK's loop.
• A ReAct agent suits open-ended problems where the step count cannot be predicted, such as research and prototypes.
• An RPA plus LLM workflow suits stable interfaces, legacy systems without APIs and structured high-volume data movement.
• A custom multi-agent framework suits breadth-first research where work parallelizes and the task is worth the token cost.

How we are measuring the five architectures

MightyBot is running a first-party benchmark of these five architectures. The same regulated document workflows run on each build type, and the results will be labeled for what they are: vendor-run, on synthetic documents, specific to the configurations tested. The RPA arm runs through a proxy, which the published method will state. No results exist yet, and this page will link to them when they are published.