How it works / Technical walkthrough

From company processes
to production AI systems.

The data, integrations, execution controls, and tests that make AI part of an operation.

This is a reference delivery model, from discovery through ongoing operation. The architecture and depth depend on your systems, risk, and use case; not every workflow needs an agent, a vector database, or a new platform.

Define the process and its economics.

A process diagram is not enough. Capture actual transactions, exceptions, handoffs, and the cost of getting them wrong.

Deliverable: A process specification, named owner, baseline metrics, and a ranked use-case backlog.
Exit criterion: There is an observable outcome and an agreed way to measure it.

Implementation details for stage 1

Trace real work

Follow representative cases from the initial event to the final system update. Record inputs, decisions, owners, systems, wait states, and exceptions.

Establish the baseline

Measure volume, handling time, cycle time, rework, and escalation rates. Estimate implementation and ongoing cost against the work a system could remove.

Define the operating contract

Specify the trigger, required inputs, allowed outputs, approval points, service expectations, and the person accountable for the result.

Establish identity, access, and source records.

AI cannot resolve a customer, job, or invoice reliably if the systems disagree about what that record is.

Deliverable: A system inventory, data model, integration contracts, and an access matrix.
Exit criterion: The required records can be read with approved access and traced back to their source.

Implementation details for stage 2

Map systems of record

Identify which platform owns each field. Define stable record IDs, cross-system mappings, required fields, and how conflicting or missing data is handled.

Connect the data plane

Use APIs, webhooks, scheduled syncs, or change feeds where supported. Use browser automation where necessary. Specify freshness, rate limits, pagination, and failure behavior.

Scope access before use

Use service identities with the permissions each workflow needs. Separate environments, store credentials in a secrets manager, and document retention and access boundaries.

Build a permission-aware knowledge layer.

Business context must be retrievable, current, and limited to what the workflow is allowed to see.

Deliverable: A documented retrieval design, approved knowledge sources, and freshness rules.
Exit criterion: Test questions return relevant evidence without crossing access boundaries.

Implementation details for stage 3

Prepare operational knowledge

Version SOPs, policies, contracts, and reference documents. Preserve source links, ownership, timestamps, and document permissions.

Retrieve only the relevant context

For document-heavy work, index searchable passages and retrieve them when needed. Combine semantic or keyword search with record filters; query structured systems directly for current transactional facts.

Keep retrieval separate from training

Retrieval-augmented generation supplies evidence at request time; it does not retrain the model. Fine-tuning is a separate choice for demonstrated behavior or performance needs. Retrieval reference ↗

Assign reasoning to the model. Keep execution explicit.

Choose the smallest architecture that can complete the job and still be tested.

Deliverable: A workflow graph, tool contracts, model configuration, and versioned instructions.
Exit criterion: Every possible action has a defined input, permission check, and failure path.

Implementation details for stage 4

Separate rules from interpretation

Use code for arithmetic, validation, required approvals, and known routing rules. Use a model for tasks such as interpreting a message, extracting document fields, or proposing a classification.

Choose workflow or agent deliberately

A workflow follows defined paths. An agent can choose tools or steps within an allowed scope. Add that flexibility only when the task requires it, with limits on steps, runtime, and spend. Architecture reference ↗

Make outputs machine-checkable

Define structured schemas and typed tool interfaces. Validate formats and business meaning before acting; schema compliance does not prove a customer ID, amount, or conclusion is correct. Handle refusals and incomplete outputs explicitly. Structured output reference ↗

Make actions durable, authorized, and recoverable.

A correct model response still needs reliable software to turn it into a business transaction.

Deliverable: Integration adapters, persisted workflow state, approval queues, and a run log.
Exit criterion: Retries cannot silently duplicate the same business action.

Implementation details for stage 5

Persist the run

Accept the event, assign a correlation ID, queue the work, and record state transitions. Set timeouts, retry policies, and a queue for runs that need intervention.

Prevent duplicate effects

Use idempotency keys or deduplication checks for write operations. Track acknowledgments and reconcile uncertain results before retrying. Plan compensating actions for partial multi-system completion.

Enforce controls outside the prompt

The server checks identity, permissions, allowed tools, and arguments. Treat documents and external messages as untrusted input. Bind approvals to the exact proposed action and revalidate if its inputs change.

Test the complete workflow against real cases.

A convincing demo is not evidence that a system can handle missing information, exceptions, or a failed API.

Deliverable: A versioned test set, evaluation report, and agreed release thresholds.
Exit criterion: The workflow meets the acceptance criteria and handles known failure cases.

Implementation details for stage 6

Build a representative test set

Use approved, appropriately redacted historical cases, including uncommon exceptions and ambiguous inputs. Define the expected outcome and unacceptable actions for each case.

Evaluate outputs and side effects

Check extracted fields, record matching, routing, tool arguments, permission boundaries, and final system state. Use deterministic checks where possible and human review for subjective judgments. Repeat model-driven tests where variability matters.

Test infrastructure and changes

Simulate timeouts, duplicate events, expired credentials, stale documents, and unavailable systems. Measure latency and cost per completed case. Re-run the suite after prompt, model, retrieval, or tool changes. Evaluation reference ↗

Introduce autonomy in measured increments.

Reading records, drafting actions, and committing actions are different permission levels.

Deliverable: A rollout plan, operator runbook, fallback procedure, and rollback controls.
Exit criterion: A named operator can stop the workflow and recover the work in progress.

Implementation details for stage 7

Start in shadow mode

Process real inputs without writing changes. Compare proposed outcomes with the existing process and review disagreements.

Move through review to bounded execution

Start with approved drafts or a limited population. Allow automatic actions only for the scope that has met its thresholds. Keep uncertain and consequential cases in a review queue.

Prepare the people and the fallback

Train owners to inspect a run, approve or reject a proposal, and resolve an exception. Define what happens during an outage, how changes are disabled, and how queued work is reconciled.

Operate the system and extend the shared foundation.

Company-wide adoption comes from reusable infrastructure and accountable process owners, not one unrestricted agent.

Deliverable: Operational dashboards, incident ownership, a regression suite, and an expansion backlog.
Exit criterion: The business can see what is running, what it costs, and who handles a failure.

Implementation details for stage 8

Observe the work, not just the model

Track completion rates, exceptions, overrides, queue age, latency, and cost. Link events, retrieved sources, model and instruction versions, tool calls, and approvals with appropriate redaction and retention.

Manage change

Assign alert and incident owners. Review failures, correct source data or workflow logic, update the test set, and deploy versioned changes with rollback.

Expand across departments

Reuse identity, connectors, record mappings, knowledge access, tool contracts, and evaluation infrastructure. Add a new workflow only after its owner, data, permissions, and success criteria are defined.

What happens when
a request enters the system.

Illustrative reference workflow. The model handles interpretation; the surrounding software handles state, permissions, and side effects.

01
Receive
A webhook delivers a customer request. Persist the event ID before processing.
02
Retrieve
Read the current customer record and the relevant, permitted process instructions.
03
Propose
Extract the request into a defined schema and propose the next permitted action.
04
Validate
Check required fields, record IDs, duplicate events, permissions, and approval rules.
05
Execute
Write the approved task through the system API and record its acknowledgment.
06
Reconcile
Link the outcome to the run. Retry safe failures or send unresolved cases to an owner.

What the company has
when the build is ready.

01 / OPERATING DESIGN

A documented process and owner.

The work, exception paths, responsibilities, and success measures are explicit.

02 / WORKING SYSTEM

Connected data and controlled actions.

The workflow can retrieve the right context and execute within defined permissions.

03 / RELEASE EVIDENCE

Tests, monitoring, and a fallback.

There is evidence it works, a way to inspect failures, and a way to stop it.

Your next move

Start with the operation.
Specify the system it needs.

Book a call

Satisfaction guarantee.

From company processes to production AI systems.