Applies to: transforming high-cognition tasks such as research, marketing, operations, analytics, and content production into measurable, correctable, reusable Agent workflows.
First decide: whether this task should be turned into an Agent
Proceed to automation only when most of the following conditions are met at the same time:
- The input and qualified output can be described;
- The expert can clearly articulate "what is right and what is wrong";
- The task occurs repeatedly, or the delivery cost grows approximately linearly with the number of clients;
- Key data can be obtained legally and reliably;
- Errors can be intercepted before publishing, payment, deletion, or sending externally;
- Results can be reviewed through rubrics, sample sets, or business metrics.
If the task is low-frequency, the goals keep changing, or there is no acceptance criteria, start with a manual SOP rather than building a multi-Agent system first.
1. Establish a baseline; do not write Prompts directly
Select 10–30 recent real tasks and record the manual baseline:
| Metric | Definition |
|---|---|
| Task success rate | Number of tasks that passed acceptance on first delivery / total number of tasks |
| First-pass rate | Number of tasks that passed without rework / total number of tasks |
| Cycle time | Elapsed time from receiving complete input to deliverable output |
| Manual hours | Person-hours spent on research, production, review, and rework |
| Cost per run | Sum of model, tool, data, and manual review costs |
| Retry rate | Proportion of tasks with tool retries or full redo |
| Severe error rate | Proportion of high-risk events such as erroneous publishing, erroneous payment, and data leakage |
Without this baseline table, you can only prove that the Agent "can run," not that the workflow has improved.
2. Break Skills out of the business chain
First draw the business chain, then break out skills according to acceptable results:
需求澄清 → 数据获取 → 证据整理 → 分析 → 产出 → 质检 → 人工批准 → 交付 → 反馈沉淀
Each skill contains at least:
name: competitor-evidence-pack
input_contract:
required: [product, market, competitors, time_window]
output_contract:
required: [claims, source_urls, captured_at, confidence, unknowns]
tools:
allow: [search, fetch]
deny: [publish, delete, payment]
acceptance:
- every material claim has a source
- source capture time is recorded
- unknown facts are labeled, not guessed
escalate_when:
- authenticated source is inaccessible
- sources conflict on a decision-critical fact
Prioritize single-responsibility skills. Add an orchestrator only when there are clear dependencies between steps.
3. Harness: let the system know boundaries, remember corrections, and continuously evaluate
A Prompt describes only one interaction; a harness manages the long-running environment. It contains at least five layers:
- Context: brand, client, goals, forbidden zones, and data permissions;
- Skills: general skills and client-specific skills are separated and invoked selectively by task;
- Memory: only confirmed preferences, errors, and corrections are retained; do not write guesses as facts;
- Evaluation: fixed sample sets, rubrics, regression tests, and business metrics;
- Observability: input summary for each step, tool calls, evidence, cost, elapsed time, retries, and final approver.
The correct way to handle a failure is not to add prompts indefinitely, but to: record the failure type → determine whether it is a data, tool, reasoning, or acceptance problem → modify the corresponding layer → regress with the old sample set.
4. Choose the orchestration method
| Pattern | Applicable situation | Main risk |
|---|---|---|
| Sequential | The later step strictly depends on the output of the previous step | Cascading upstream errors |
| Parallel | Multiple independent sources or options can be produced simultaneously | Merge conflicts, duplicate costs |
| Routing | Different tasks should invoke different expertise | Misclassification |
| Supervisor—Executor | Tasks can be split into multiple independent subtasks | The supervisor becomes a bottleneck |
| Review—Revision | Outputs have clear rubrics and can be iteratively improved | Unbounded loops, runaway costs |
Start by default with a single Agent + multiple skills. Only upgrade to multi-Agent when observational data proves that throughput or expertise isolation genuinely requires concurrency.
5. Human intervention and permissions
The following actions require human approval by default:
- External publishing, mass messaging, private messaging, or speaking on behalf of an individual;
- Payments, refunds, procurement, and price commitments;
- Deleting, overwriting, or batch-modifying data;
- Using personal data without authorization;
- Low-confidence conclusions that will affect customer decisions.
Do not repeatedly request the same authorization for consecutive tasks; record the authorization subject, scope, and validity period. When permissions are insufficient, return the missing items and recovery path; do not pretend the task is complete.
6. Launch thresholds and rollback
Proceed in four stages:
- Shadow: The Agent generates results but does not affect human delivery;
- Copilot: Humans select, modify, and approve each output;
- Guarded automation: Low-risk steps execute automatically, high-risk actions require approval;
- Autonomous: Used only for boundary tasks that have stably passed regression testing, are fully auditable, and can be rolled back.
For each version change, compare the success rate, cycle time, manual labor hours, cost, and serious error rate on the same batch of tasks. If any safety metric deteriorates, roll back to the previous stable version.
Real case: Marketing insight delivery moving from project-based to productized
Source: Gingiris Feishu meeting minutes, "Analysis of AI agent practice implementation dilemmas and scaling attempts," 2026-05-10. The following are experiential statements from the meeting, not an independent audit or randomized controlled experiment.
Old pipeline
- Operations staff manually browsed Xiaohongshu for about 10 days to 2 weeks;
- Early automation compressed information collection to a few hours, but reports still relied on manual thinking, iteration, and charting;
- One high-quality PPT delivery required about 15 people and 3–4 weeks, making it hard to replicate for 100 or 1,000 clients.
New pipeline
- Split influencer screening, track analysis, audience identification, and content and placement recommendations into general skills and client-specific skills;
- Put positive examples, negative examples, acceptance criteria, memory, and feedback loops into the harness;
- The system first identifies the task, then combines and invokes skills; experts are responsible for strategic judgment and final acceptance;
- Results, the insight process, and error corrections remain in the system for reuse next time.
Reported results
- About 15 people × 3–4 weeks PPT projects became 1 strategist + AI system, passing on the first try in 5 days；
- A competitive analysis and placement recommendation can be delivered in less than 1 day ;
- The service cost of meeting reports decreased by two orders of magnitude；
- compared with earlier methods. Professional strategists still need about 2–3 months to learn and migrate to the new way of working, showing that experts have not been "replaced at zero cost."
What this case actually validates
It supports the judgment that "encoding expert judgment as skill + harness can reduce delivery cycles and marginal manual effort"; it does not prove that all industries can achieve the same magnitude, nor does it disclose error rates, model costs, and long-term customer retention under a consistent methodology. Therefore, when reusing it, you must re-establish your team's baseline and additionally test quality, retries, and severe error rates.
7-day implementation checklist
- Day 1: Choose a high-frequency task, collect 10–30 real examples and a manual baseline;
- Day 2: Define inputs, outputs, rubric, prohibited areas, and manual approval points;
- Day 3: Break it down into 3–7 single-responsibility skills;
- Day 4: Integrate evidence recording, logs, costs, and failure classification;
- Day 5: Shadow replay, fix the most frequent failures;
- Day 6: Run Copilot at small scale, compare against the manual baseline;
- Day 7: Decide to continue, roll back, or automate only one segment.
Final output template
Every workflow review must deliver:
- Business chain and automation boundaries;
- Skill inventory and input/output contracts;
- Orchestration diagram and tool permissions;
- Evaluation set, baseline, and current results;
- Manual intervention, failure recovery, and rollback plan;
- An experiment plan that changes only one variable in the next round.
Built by Gingiris. Historical figures are labeled as reported case evidence; do not present them as guaranteed outcomes.