LogicFolds field notes
Useful thinking for teams putting AI into real operations.
Workflow design, decision controls, integrations, and the work that turns an AI demo into a dependable system.
Latest articles
The operating layer of AI.
The blind spots in using an LLM to judge your AI workflow
LLM judges make grading AI workflow output cheap at scale, but they carry real biases. Here is what to check before you trust the score.
Roll out an AI workflow change to a slice of cases first
Why a full cutover is the wrong way to ship a prompt, model, or logic change, and how canarying limits the damage when it's wrong.
Set an error budget for your AI workflow before you scale it
Borrow the SRE idea of an error budget to decide, in advance, how much AI workflow failure is tolerable and when to pause shipping changes.
Retiring an AI workflow needs a plan, not just an off switch
A kill switch stops a bad run. Retiring a workflow for good is a separate, planned process with its own checklist.
Stop putting API keys in your AI workflow's prompts and logs
Secrets leak into AI workflows through prompts, tool configs, and debug logs. Here is where to find them and how to get them out.
When one approver isn't enough for an AI workflow decision
Some AI-assisted decisions need two separate people to sign off, not one reviewer rubber-stamping a suggestion.
Why an AI workflow decays even when nobody touches the code
Retrieval ranks by similarity, not recency. If nobody owns the knowledge base, your AI workflow quietly starts answering from superseded documents.
Plan the handoff before the AI workflow build team leaves
A launched AI workflow needs a named owner, documented judgment calls, and a maintenance plan before the people who built it move on.
Every AI workflow needs a kill switch, not just a review step
Human review protects one decision. A kill switch protects the whole operation when a step starts failing in production.
Shadow-run an AI workflow change before you cut it over
Route a slice of real traffic to a workflow change before full cutover, compare it against the current version, then decide.
Track cost per completed outcome, not cost per API call
Token and API spend don't say whether an AI workflow is worth running. Divide cost by completed outcomes instead, and track the trend.
Pin your model version in production, don't float on latest
An alias that always points to the newest model can change your workflow's behavior with no code change. Pin a dated version and schedule re-qualification.
The runbook your AI workflow needs before it goes live
Monitoring tells you something broke. A runbook tells the on-call person what to do about it in the next five minutes.
Build vs. buy: how to decide for your first AI workflow
A framework for choosing between building an AI workflow in-house and buying a platform, and what each path actually commits you to.
Send the model only what the step needs, not the whole record
Why scoping every AI workflow step's input to the minimum required fields cuts exposure without cutting capability.
Why AI workflow integrations fail quietly at the auth layer
Expired tokens and narrowed permissions rarely throw a clear error. They make a workflow look idle or inaccurate instead.
Batch or real-time: the right mode for an AI workflow
The decision isn't about speed. It's about event shape, latency tolerance, and what recovery looks like when a run fails.
Not every step in your AI workflow needs the same model
Routing each step to the smallest model that can do the job cuts cost and latency without touching accuracy where it matters.
Set a latency budget before you wire an AI step into a workflow
An AI step without a timeout is a liability. How to set per-step latency budgets so one slow call doesn't stall the whole handoff.
What happens to your AI workflow when the model provider goes down
A circuit breaker, a fallback path, and a clear timeout policy keep one provider outage from stalling every workflow that depends on it.
Never let an AI workflow act on an unverified webhook
An inbound webhook is an unauthenticated HTTP request until proven otherwise. Verify it before an agent reads it as fact.
Why AI workflow outputs need schema validation before write-back
A model's reply is not a decision until it passes a strict schema check. Where that gate belongs, and why prompting alone can't be the control.
How long should an AI workflow keep its decision evidence?
A retention plan for the evidence, prompts, and outputs behind automated decisions, so records exist when needed and disappear when they should not.
Webhooks vs. polling: picking the right trigger for an AI workflow
How an AI workflow learns that something happened shapes its cost, latency, and failure modes long before the model runs.
Why your AI workflow's prompts belong in config, not code
Hardcoded prompts turn a wording fix into a full deploy. Treating them as versioned config gives you faster, safer changes.
Reviewer disagreement is a signal, not noise, in AI workflows
When two reviewers approve different cases the same way, that gap is diagnostic data about your rubric, not a training problem.
Shadow AI is a governance gap, not an adoption failure
Employees already use AI tools your workflow never sanctioned. Closing the gap takes a faster governed path, not a policy memo.
Why a single confidence threshold fails your AI workflow
A model's confidence score is not a probability of being right. Here is how to set and govern thresholds that actually hold up.
How many cases you need before calling a workflow improved
A handful of good outcomes after a change is not proof. Here is how to size and time the comparison so the result can be trusted.
Design the AI workflow so the model is not welded to it
Model choice should be a configuration change, not a rebuild. Here is where that boundary belongs in the architecture.
Why an AI workflow must treat what it reads as data, not orders
Prompt injection isn't a hypothetical. It's what happens when a workflow lets an email, ticket, or web page it reads issue it instructions.
Give every tool call in your AI workflow its own permission scope
Most agentic failures are not bad model outputs. They are tools that were allowed to do more than the task required.
Do you need a real orchestration engine, or just better glue code?
A practical test for when scripted automation stops being enough and an AI workflow needs a real state machine underneath it.
Designing an AI workflow to survive rate limits and backpressure
Rate limits are not an edge case. Here is how to design retries, batching, and backpressure so a workflow degrades instead of failing.
Why AI workflows need a real audit trail, not a log file
A log file records that something happened. An audit trail proves who approved it, on what evidence, and why. Agentic workflows need the second one.
Build a golden test set before you change an AI workflow
A fixed set of real cases with expected outputs is what tells you a prompt or model change helped instead of just feeling different.
Why idempotent steps matter more than retries in AI workflows
A retry is only safe if repeating the step is safe. Here is how to design AI workflow steps so retries never duplicate a charge, ticket, or message.
How to scope an AI workflow pilot before you roll it out further
A pilot only earns a wider rollout if its scope, success criteria, and monitoring are set before it starts, not after.
Why AI workflows break when the tools behind them change
API updates, field renames, and auth rotations are routine for vendors and quiet failure points for your automation.
How to prioritize AI automation opportunities
A scoring method for ranking candidate AI workflows by impact, feasibility, data readiness, and risk, instead of building whichever idea is loudest.
How to change an AI workflow that is already live
A production workflow is never finished. Here is how to version, stage, and roll back changes without breaking what already works.
What to put in writing before an AI workflow project starts
Most AI workflow rework traces back to a vague scope. Here is what to specify in writing before the build begins.
What to monitor when an AI workflow runs in production
Traces, metrics, and events worth capturing so an agentic workflow's failures are visible before they become incidents.
Treat AI workflow failures like production incidents
When an agentic workflow acts wrong, the fix that holds is containment and root cause, not a quiet prompt edit.
How to know an AI workflow is actually improving
Without a baseline and a small set of tracked metrics, an AI workflow's impact is a guess dressed up as a result.
AI adoption fails on trust, not on the technology
In industries slow to adopt AI, the real barrier isn't the model. It's fear, disrupted workflows, and metrics nobody already tracks.
Design the human-AI split before you scale the pilot
Research on real deployments shows why bolting AI onto an existing workflow backfires, and what to decide before scaling a pilot.
The productivity dip every AI workflow rollout hits first
Adoption counts are not value. What to baseline before launch, why output usually dips before it improves, and which KPI actually prices the result.
Four operational controls most agentic AI builds skip
Identity, capacity limits, spend caps, and security oversight are unglamorous, but they decide whether an agent is safe to run in production.
What orchestration does in an AI workflow
The model answers one question at a time. Orchestration is the layer that turns those answers into a reliable, multi-step business process.
The integration layer most AI projects skip
A model that can draft or classify is not connected to your business until it can read and write through a defined, permissioned interface.
Decision controls set the boundary an AI workflow can't cross
Functionality, permissions, and autonomy are three separate dials. Set all three deliberately before an AI workflow is allowed to act on its own.
Where AI opportunities actually hide in your operations
Most useful AI candidates are not the flashy ones. They sit in the repetitive, evidence-heavy steps your team already complains about.
The evaluation layer that separates pilots from production
A convincing demo is not proof a workflow holds up in production. Here is what a real evaluation practice checks, and how to build one.
What an AI workflow costs after the pilot
The build is the visible expense. Evidence plumbing, review time, evaluation and ownership are what decide whether the workflow survives its first year.
Five checks before you automate a workflow with AI
A practical test for separating a valuable operational system from an expensive demo.
Human review is a workflow step, not a safety disclaimer
Where review belongs, what the operator needs to see, and how the process should recover.
What turns an AI demo into a dependable operation
The missing layer is usually orchestration, evidence, permissions, evaluation, and recovery.