Most agent programmes do not stall because the model is weak. They stall because the workflow has not been decomposed into the right decision and execution layers.<\/p>\n\n\n\n
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A useful agentic AI architecture starts with the work itself: what is deterministic, what must be retrieved, where judgement is required, and where the system is allowed to take action.<\/strong><\/td><\/tr><\/tbody><\/table><\/figure>\n<\/blockquote>\n\n\n\n Across agentic AI builds, a recurring failure pattern is to select a model, add a chat interface, connect a document store and only then discover that much of the real work was never in the documents. It was in approval chains, exception handling, policy thresholds and the judgement people apply when the available information is incomplete. When that happens, the model is often blamed for poor accuracy, high latency or inconsistent outcomes. In practice, the root cause can be a workflow that was never decomposed clearly enough for automation<\/p>\n\n\n\n
Decompose the workflow before writing orchestration code<\/h1>\n\n\n\n
Before a line of orchestration code is written, classify every step in the business process. A practical decomposition separates four kinds of work:<\/p>\n\n\n\n
Step type<\/strong><\/td> What it means<\/strong><\/td> Where it belongs<\/strong><\/td><\/tr><\/thead> Deterministic<\/td> Fixed business rules, thresholds and calculations<\/td> Application code – not inside the LLM<\/td><\/tr> Retrieval-bound<\/td> The answer exists in an approved corpus and must be cited<\/td> Grounded retrieval with source references<\/td><\/tr> Judgement-bound<\/td> Requires interpretation or professional opinion<\/td> Agent proposes; named human disposes<\/td><\/tr> Action-bound<\/td> Writes into a system of record<\/td> Transaction boundary with rollback and audit<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n This separation matters because each layer has a different control model, test method and failure mode. Deterministic logic should be tested like conventional software. Retrieval should be evaluated for grounding and citation quality. Judgement requires clear human decision rights. Actions require transaction controls, auditability and rollback.<\/p>\n\n\n\n
What changes when the process is decomposed properly<\/h2>\n\n\n\n
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- Cost and latency improve. On one build, moving deterministic steps back into code reduced model calls from 11 per transaction to 4 – roughly 60% lower inference cost per transaction – while median latency fell from 18 seconds to under 7 seconds.<\/li>\n\n\n\n
- Accuracy becomes easier to diagnose. In a recent UAT cycle, two of three accuracy defects being investigated were traced to missing business rules rather than hallucination.<\/li>\n\n\n\n
- Human-in-the-loop becomes an operating control. The human checkpoint is designed with a named owner instead of being added as a generic disclaimer before go-live.<\/li>\n\n\n\n
- Testing becomes more efficient. The deterministic layer can be validated with conventional test cases, while evaluation tooling is concentrated on the retrieval and judgement layers where model behaviour actually matters.<\/li>\n<\/ul>\n\n\n\n
A practical design sequence for agentic AI workflows<\/h2>\n\n\n\n
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- Map the end-to-end process. Capture the real operating flow, including approvals, exceptions, rework and hand-offs – not only the happy path.<\/li>\n\n\n\n
- Classify every step. Mark each step as deterministic, retrieval-bound, judgement-bound or action-bound.<\/li>\n\n\n\n
- Move fixed rules into code. Do not spend model calls on thresholds, calculations or business logic that can be expressed deterministically.<\/li>\n\n\n\n
- Ground retrieval in approved sources. Make source references part of the answer design where evidence, policy or precedent matters.<\/li>\n\n\n\n
- Define human decision points. For judgement-bound steps, name the person or role that approves, rejects or overrides the agent output.<\/li>\n\n\n\n
- Select and orchestrate models last. Once the workflow, controls and evidence needs are clear, model selection becomes an architecture decision rather than the starting point.<\/li>\n<\/ul>\n\n\n\n
What technology leaders should ask before approving an agent programme<\/h2>\n\n\n\n
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- Can the team draw the process as a state machine, including exceptions and approval paths?<\/li>\n\n\n\n
- Which steps are truly probabilistic, and which should remain deterministic?<\/li>\n\n\n\n
- Where must every answer be grounded in an approved source?<\/li>\n\n\n\n
- Which decisions require a named human owner?<\/li>\n\n\n\n
- What system actions need rollback, audit and transaction boundaries?<\/li>\n\n\n\n
- What metrics will prove the workflow is improving – before increasing autonomy?<\/li>\n<\/ul>\n\n\n\n
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PRACTITIONER VIEW
<\/strong>If a team cannot draw the process as a decomposed state machine, they are not ready to build agents on it. Model selection is a week-four decision, not a week-one decision.<\/td><\/tr><\/tbody><\/table><\/figure>\n<\/blockquote>\n\n\n\nThe executive takeaway<\/h2>\n\n\n\n
Agentic AI is not simply a model wrapped around enterprise data. It is a redesign of how work is divided between deterministic systems, retrieval, machine judgement and human accountability. The quality of that decomposition directly influences cost, latency, testability, governance and adoption.<\/p>\n\n\n\n
Start with the process. Make decision rights explicit. Instrument the workflow. Then decide where an LLM or agent adds real value.<\/p>\n\n\n\n
Frequently asked questions<\/h2>\n\n\n\n
What is process decomposition in agentic AI?<\/strong><\/h3>\n\n\n\n
It is the practice of breaking a workflow into distinct step types – deterministic, retrieval-bound, judgement-bound and action-bound – so that each step is implemented with the right technology and control model.<\/p>\n\n\n\n
Why should deterministic rules stay outside the LLM?<\/strong><\/h3>\n\n\n\n
Fixed rules, thresholds and calculations are more predictable, testable and economical in application code. Keeping them out of the model reduces unnecessary inference calls and makes defects easier to isolate.<\/p>\n\n\n\n
When should model selection happen?<\/strong><\/h3>\n\n\n\n
After the workflow, evidence requirements, human decision points and system actions are understood. That allows model choice to be based on a defined workload instead of driving the design prematurely.<\/p>\n\n\n\n
Planning an Agentic AI initiative?<\/strong><\/h2>\n\n\n\n
SYNAPLAB helps leadership teams connect business process, AI architecture, governance and measurable transformation outcomes. Discuss where agents can create value – and where deterministic systems or human judgement should remain in control.
www.synaplab.io\u00a0 |\u00a0 connect@synaplab.io<\/strong><\/p>\n","protected":false},"excerpt":{"rendered":"Most agent programmes do not stall because the model is weak. They stall because the workflow has not been decomposed into the right decision<\/p>\n","protected":false},"author":4,"featured_media":91,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[6],"tags":[],"class_list":["post-81","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-data"],"_links":{"self":[{"href":"https:\/\/synaplab.io\/insights\/wp-json\/wp\/v2\/posts\/81","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/synaplab.io\/insights\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/synaplab.io\/insights\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/synaplab.io\/insights\/wp-json\/wp\/v2\/users\/4"}],"replies":[{"embeddable":true,"href":"https:\/\/synaplab.io\/insights\/wp-json\/wp\/v2\/comments?post=81"}],"version-history":[{"count":8,"href":"https:\/\/synaplab.io\/insights\/wp-json\/wp\/v2\/posts\/81\/revisions"}],"predecessor-version":[{"id":89,"href":"https:\/\/synaplab.io\/insights\/wp-json\/wp\/v2\/posts\/81\/revisions\/89"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/synaplab.io\/insights\/wp-json\/wp\/v2\/media\/91"}],"wp:attachment":[{"href":"https:\/\/synaplab.io\/insights\/wp-json\/wp\/v2\/media?parent=81"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/synaplab.io\/insights\/wp-json\/wp\/v2\/categories?post=81"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/synaplab.io\/insights\/wp-json\/wp\/v2\/tags?post=81"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}