Agent Template · self-correcting loop · agents calling agents · bounded iterations
Editorial loop: A Self-Correcting Agent
A self-correcting agent: a bounded LOOP re-runs a writer-critic child agent — draft, harsh self-critique, rewrite — until the child approves its own draft or three rounds are up. The loop is durable (it survives restarts mid-revision) and every round is visible in the run waterfall.
How It Works
A bounded loop re-runs a writer-critic child agent — draft, harsh self-critique, rewrite — until the child approves its own draft or three rounds are up. Every round is its own child run in the trace.
The loop is durable, so it survives a restart mid-revision. The parent pins the child by version: editing the child publishes a new version, and the parent keeps the pinned one until you re-pin.
Try It
- In TheYgent, open Settings → Samples.
- Fill the model slots below with models from your registry, then click Install on Editorial loop.
- Open the installed agent and run it with the example input.
A thermos that brews tea at exactly 80 degrees.
The Graph
Every node in the agent and where its output goes, read from the template itself.
sample-editorial-loop
| Node | Type | Sends its output to |
|---|---|---|
| Topic | input |
Revise until approved |
| Revise until approved | loop |
Extract the final draft |
| Extract the final draft | transform |
Final blurb |
| Final blurb | output |
— |
Child agent: sample-editorial-writer
| Node | Type | Sends its output to |
|---|---|---|
| Topic or prior round | input |
Write + critique |
| Write + critique | llm |
Round result |
| Round result | output |
— |
More Templates
Hybrid reasoner
Two Models in One Run — Two models in one run: a local triage model classifies every request and a router sends easy ones to your local model and hard ones (coding, analysis, long-form) to an API model.
See the template → data privacyPrivate SQL analyst
Data Privacy by Construction — A data-privacy pattern: the API model sees ONLY the database schema and your question — never a single row — and writes a SELECT statement; a deterministic guardrail refuses any statement that does not start with SELECT; the query then runs on a local SQLite MCP server and a LOCAL model reads the rows and answers.
See the template → human in the loopExpense approver
Human in the Loop — Human in the loop with a real approve/reject: a local model assesses the risk of an expense request, the run then WAITS durably for a human decision, and a router either executes the (placeholder) payment step or refuses.
See the template → MCP from OpenAPI (REST)Tool belt
Step Tools vs. LLM-Driven Tools — The two ways to use a tool, in one agent: a GraphQL API turned into an MCP server runs as a flow STEP (it always executes), while a REST API turned into an MCP server is WIRED TO the local model as a capability the model decides to call.
See the template → multi-model consensusSecond opinion
Multi-Model Consensus — Ask a local model AND an API model the same question independently, then a local judge reconciles the two answers into one — flagging any disagreement.
See the template → PII firewallPII firewall
Privacy as a Router — A privacy router: a deterministic PII guardrail inspects every request BEFORE any model runs.
See the template → vision in a pipelineVisual inspector
Vision in a Pipeline — Vision inside a pipeline, not a chat window: a local vision model inspects an image against your acceptance criteria, emits a structured pass/fail verdict, and a router sends the run down the matching branch — the skeleton of a visual QC line.
See the template → speech-to-textVoice desk
Voice In, Voice Out — Voice in, voice out: speech-to-text transcribes the audio, a model answers in spoken prose, text-to-speech reads it back.
See the template → local image generationArt department
Local Image Generation — Local image generation in a pipeline: a local model turns your rough brief into one vivid, concrete image prompt, and a local diffusion model paints it.
See the template → local RAG (pgvector)Handbook Q&A
Retrieval as a Model Capability — Retrieval as a model capability, fully local: the installer creates a retrieval source in your own Postgres (pgvector), seeds it with a fictional TheYgent company handbook, and wires it to a local model as a search tool the model decides to call — answers cite the handbook section they came from.
See the template → parallel fan-out (map)Batch briefing
Durable Fan-Out with a Cost Ceiling — Durable parallel fan-out with a cost ceiling: a MAP node fans one child agent out per topic (two at a time), collecting a brief for each — a crashed worker resumes only the unfinished branches.
See the template →