Open by design
The patterns underneath the apps.
The apps are very different — trading, support, voice, BI, this site. The agents inside them aren’t. They follow the same contract: trigger, input, execution, eval, ops, output. That standardization is the studio. The app code stays private; the patterns don’t.
What follows is the working set — nine patterns that show up across every product I build. Some of them are running right here on this page.
Source Connector
Pull anything in. Normalize. Keep current.
The pattern every app starts with — connect to a third-party data source, pull schemas and rows, normalize into the studio's canonical shape, and keep it incrementally synced. The connector contract is the same whether the source is Shopify, Notion, Gmail, or a brokerage feed.
The contract
- Trigger
- Schedule (15-min default) + manual backfill
- Input
- OAuth credentials + last-synced cursor
- Execution
- Provider client → pagination loop → schema normalizer
- Eval
- Row-count parity vs. provider, schema-drift assertions
- Ops
- Per-source error budget, dead-letter queue, on-call alert on drift
- Output
- Canonical rows in Postgres, indexed for retrieval
Where it runs
- ClearfraimShopify, Stripe, Meta Ads, Notion, Sheets
- AtlasNotion, Drive, Slack, Gmail
- IMPGInstagram, TikTok, X
dataingestioninfra
Router
Classify the inbound. Send it to the right place.
A small, fast classifier that reads anything coming in — a question, an email, a call transcript, a ticket — and decides which downstream agent or queue should handle it. Cheap, evaluated, and the same pattern across very different surfaces.
The contract
- Trigger
- Inbound message, email, call, or ticket
- Input
- Raw text + metadata (channel, sender, history)
- Execution
- Small-model classifier → label set → confidence score
- Eval
- Frozen labeled set, F1 per class, abstain rate
- Ops
- Confidence threshold gates routing; below threshold falls back to human
- Output
- Label + confidence + suggested next agent
Where it runs
- ClearfraimRoutes business questions to the right answerer
- TriageRoutes inbound to the right human queue
- ConciergeDecides answer-now vs. open-ticket vs. escalate
- SajaRoutes calls to the right voice agent or human
- jasonki.comOn this siteSage qualifies inbound on /chat and routes to the right service tier
routingclassification
RAG Answerer
Answer from your data. Cite the source. Refuse when unsure.
The retrieval-grounded agent that powers every answering surface — BI questions, support tickets, knowledge search. Always returns a citation. Always allowed to say it doesn't know. Same harness, same eval shape, same refusal threshold across apps.
The contract
- Trigger
- User question, ticket, or upstream agent call
- Input
- Question + retrieval scope (which sources are in-bounds)
- Execution
- Embed → retrieve → rerank → generate with citations
- Eval
- Faithfulness, citation correctness, refusal rate on out-of-scope
- Ops
- Refusal floor enforced; low-confidence answers go to a human
- Output
- Answer + citations + confidence + sources consulted
Where it runs
- ClearfraimAnswers business questions from connected sources
- ConciergeAnswers customer support questions from docs + tickets
- AtlasAnswers personal-knowledge questions across your stack
- jasonki.comOn this siteSage answers visitor questions from the studio's own docs + service tiers
ragretrievalanswering
Trace Logger
Every decision an agent makes is auditable.
Agents are opaque by default. The trace logger isn't — it captures the reasoning chain, the tools called, the documents retrieved, and the model output for every decision. Same schema across every agent in the studio so the eval and drift surfaces can read from one place.
The contract
- Trigger
- Wraps every agent call automatically
- Input
- Agent invocation context
- Execution
- Captures prompt, retrieved context, tool calls, output, tokens, latency
- Eval
- Trace completeness assertion in CI
- Ops
- Hot store for 30 days, cold store for 1 year, redaction for PII
- Output
- Structured trace row queryable by run_id, agent, app, tenant
Where it runs
- 67QuantReasoning trail for every trading signal
- ClearfraimSource-attribution trail for every BI answer
- ConciergeDecision trail for routing + escalation
- jasonki.comOn this siteCaptures every Sage turn into chat_messages with token + latency metadata
observabilityevals
Drift Watcher
Catch the moment an agent stops performing.
Agents drift. Models update. Data changes. The drift watcher runs the frozen eval set on a schedule, compares against baseline, and pages when any metric crosses its band. This is what makes the retainer real — it's not human-watching, it's an agent watching the other agents.
The contract
- Trigger
- Schedule (hourly) + on every model or prompt change
- Input
- Frozen eval set + current production agent
- Execution
- Replay eval set → score → compare to baseline bands
- Eval
- Self-test: does the watcher catch a known regression?
- Ops
- Page on amber, halt on red, write incident with the failing examples
- Output
- Drift report + diff vs. baseline + suggested rollback target
Where it runs
- ClearfraimWatches RAG faithfulness + answer latency
- 67QuantWatches signal calibration + execution slip
- SajaWatches transcription accuracy + handoff rate
evalsopsmonitoring
Gatekeeper
Human-in-the-loop for the calls that matter.
Not every decision should be automatic. The gatekeeper is the standardized escape hatch — a queue, a review surface, and a feedback loop that turns the human's call into training signal. Same UI, same SLAs, same retraining cadence wherever it shows up.
The contract
- Trigger
- Confidence below threshold or category flagged for review
- Input
- Pending decision + agent's reasoning + supporting context
- Execution
- Reviewer accepts, edits, or rejects; edits become training data
- Eval
- Reviewer-agreement rate, time-to-decision, escalation accuracy
- Ops
- On-call rotation, SLA per channel, weekly retrain on edits
- Output
- Final decision + reviewer rationale + training pair
Where it runs
- TriageReviews high-stakes routing before it sends
- ConciergeReviews ambiguous answers before they ship
- SajaApproves transfers and refund-class calls
- jasonki.comOn this siteEvery blog comment lands as pending until manually approved via PATCH /api/comments/{id}
hitlopstraining
Scheduler
Agents run on triggers, not vibes.
The orchestration layer that fires agents on cron, webhook, or upstream completion. Standardized retries, jitter, dead-letter routing, and idempotency keys — so a missed run is recoverable and a duplicate run is safe.
The contract
- Trigger
- Cron, webhook, or upstream event
- Input
- Job spec + idempotency key + payload
- Execution
- Queue → worker → retry-with-backoff → dead-letter on exhaustion
- Eval
- Schedule adherence, dead-letter rate, duplicate-run rate
- Ops
- Dashboard per job, alarm on dead-letter spike
- Output
- Run record + status + downstream agent invocation
Where it runs
- 67QuantFires signal generation on market-data ticks
- IMPGFires content drafting + posting on schedule
- TriageFires follow-up nudges on stale threads
orchestrationinfra
Asset Generator
Brand-coherent visuals on demand, from a single frozen prompt.
A small CLI agent that composes brand prompts and fans out to image and video models. Every demo cover, post cover, and hero reel on this site is generated through it — the prompt prefix is frozen so the look stays coherent across regenerations.
The contract
- Trigger
- CLI invocation or scheduled brand sweep
- Input
- Entity (demo / post / service) + frozen brand prompt + motif
- Execution
- Compose prompt → call Gemini Flash Image or Veo → write to public/ → wire metadata
- Eval
- Brand-coherence review by the brand-assets agent + visual diff against baseline
- Ops
- Costs logged per call, regeneration is idempotent on entity slug
- Output
- Image or video file + auto-wired entry in demos.ts or post frontmatter
Where it runs
- jasonki.comOn this sitegen:cover and gen:hero produce every cover image and the hero reel
- IMPGGenerates post imagery for creator brands on schedule
assetsbrandpipeline
Eval Runner
Every agent ships with a test set it has to pass.
The harness that runs an agent's frozen examples on every change — prompt edit, model swap, tool addition. Pass-fail gates the deploy. The same runner shape works for a routing classifier, a RAG answerer, or a voice agent transcript.
The contract
- Trigger
- Pre-deploy hook + on-demand from CLI
- Input
- Agent under test + frozen example set + scoring rubric
- Execution
- Replay examples → score → diff vs. baseline → pass/fail report
- Eval
- Self-test: does the runner correctly fail a known-bad change?
- Ops
- Block deploy on fail, attach report to PR, archive in eval store
- Output
- Pass/fail + per-example scores + diff report
Where it runs
- ClearfraimGates RAG answerer + router deploys
- 67QuantGates strategy + signal-generator deploys
- ConciergeGates answerer + escalation deploys
evalsci