SERVICE · AGENTIC WORKFLOW
Agentic Workflow
Cut cross-department review cycles from days to minutes. Supervised agents handle routine cases and escalate exceptions to humans, every run logged, every rollout gradual.
DEFINITION · WHAT AN AGENTIC WORKFLOW IS
What is an agentic workflow?
An agentic workflow is a process-automation model built around supervised agents. An AI agent follows your existing SOPs to handle clear-cut, low-risk routine cases automatically, with a confidence threshold set at every decision point, and the moment a case exceeds its boundaries or confidence is too low, it escalates to the right human with full context attached. Unlike a chatbot, it reads and writes across your systems and executes multi-step tasks, with every run fully logged, replayable, and auditable. It rolls out in graduated traffic: a small share runs first with humans reviewing, then scales up only once it's proven stable.
Supervised agents
The agent isn't given free rein. It operates within rules and risk boundaries you define. Routine cases finish automatically; exceptions escalate to a human with the reasoning attached, reserving human time for the few cases that truly need judgment.
Confidence-threshold driven
Every decision point carries a confidence threshold and risk boundary. When an amount is too high, terms are abnormal, data is missing or confidence is low, the agent stops and escalates rather than forcing a call it isn't sure about.
Fully logged and auditable
Each run records the input, the data it cited, the rule it applied, the reasoning, and whether the agent completed it or who reviewed it. Any case can be replayed to satisfy internal audit, compliance and dispute evidence needs.
Graduated rollout
A new workflow doesn't flip to full automation at once. It starts at 5% of traffic with humans reviewing, scaling to 100% against pass metrics, and any anomaly can be rolled back to human review with one click.
COMPARISON · AGENTIC WORKFLOW vs TRADITIONAL RPA
How is an agentic workflow different from traditional RPA / automation scripts?
Traditional RPA and rule-based scripts excel at repeating fixed steps, but a shifted field, a different document format, or any off-script situation makes them stall or fail. An agentic workflow can interpret unstructured content, make bounded judgments, and escalate to a human when it isn't sure. It isn't a faster script. It's an executor that reasons, leaves an audit trail, and asks for help.
Traditional RPA / rule-based scripts
Agentic workflow (delivered by Tenten)
Unstructured input / judgment
Handles only structured fields and fixed flows; can't reason over free text or ambiguous terms
Reads emails, contracts and complaints, making bounded judgments within rule limits
Exception handling
Errors out and halts on off-script cases; work gets stuck or silently dropped
Recognizes the exception and escalates to the right human with full context; never forces it, never drops it
Rule change / maintenance cost
Breaks when an interface or rule changes; engineers must rewrite scripts, and maintenance cost keeps piling up
Escalation and review are tunable rules the business can adjust, with more resilience to interface changes
Escalation & human-in-the-loop
No built-in human handoff; exceptions can only be cleaned up manually after the fact
Human-in-the-loop is the design core: agent handles routine, human handles exceptions, with reasoning attached at handoff
Audit trail & auditability
Usually only execution logs; hard to reconstruct why a decision was made
Records input, cited data, applied rule, and reasoning; any case can be replayed for evidence
Rollout (graduated)
Typically a one-shot switch to full automation; errors hit broadly and are hard to contain quickly
Graduated rollout from 5% upward, with one-click rollback to human review on any anomaly
System integration
Often mimics clicks via screen scraping; brittle and prone to break on UI updates
Reads and writes ERP/CRM/ticketing/approval systems via API under existing permissions; more stable and reliable
Let agents run the process without handing over control
For most enterprises, the bottleneck isn't a lack of AI. It's work stuck waiting on a human. A refund, a contract, an invoice, a customer complaint bounces across three or four departments, queuing for review at every stop. Each person's judgment takes two minutes, yet the case sits in your system for days. The cost isn't headcount. It's the stretched cycle time, and the customers and deals that lose patience while they wait.
We don't drop a chatbot on you. We break the workflow into steps an agent can execute, then set explicit decision boundaries for each one. Routine, low-risk, rules-clear cases finish automatically. Cases that are too large, off-policy, or low-confidence go to the right person with the full reasoning attached. The agent absorbs the bulk of repetitive cases, so your people only touch the ones that genuinely need judgment.
We embed as Forward Deployed Engineers. Our engineers sit inside your operations and wire into the ERP, CRM, ticketing, and approval systems you already run. Every agent run leaves a full audit trail you can replay, review, and assign accountability for. Rollout is gradual: the agent starts on a sliver of traffic with humans reviewing alongside, and scales only once it's proven stable. You get a faster cycle, and an answer for every decision it made.
Capabilities
01
Supervised human-in-the-loop agents
Agents execute routine cases against your existing SOPs, with a confidence threshold and risk boundary at every decision point. The moment a case crosses that boundary (too large, off-policy, missing data, low confidence) it goes to the right reviewer with full context, instead of guessing when it shouldn't.
02
Gradual, canary rollout
New workflows never flip straight to full autonomy. We start at 5% of traffic, agent handling and humans reviewing, then scale to 25%, 50%, 100%, with clear pass criteria at each stage. Any anomaly rolls the workflow back a level or returns it to manual on the spot.
03
A full audit trail for every run
Every run records what came in, which data it cited, which rule it applied, why it decided as it did, and whether it auto-completed or escalated. Any case can be replayed, enough to satisfy internal audit, compliance, and complaint investigations.
04
Real-time delivery brief per run
After each cycle the agent produces a structured brief: how many cases it processed, the auto-completion rate, which exceptions it escalated and why, and the cycle time saved. Operations leads see the live health of the process and where the bottleneck sits, without digging through logs.
05
Wires into your existing systems
Agents connect to the ERP, CRM, ticketing, approval, and messaging systems you already use, including Slack and email, reading and writing through your APIs and existing permissions. The workflow runs in your environment. Staff don't change their interface and data doesn't move.
06
Configurable escalation and review rules
Which cases auto-complete, which escalate, who reviews, and the response SLA are rules you set and adjust at will, not hard-coded logic. As the agent proves out, you widen the automation scope step by step. If a risk event hits, you tighten it in one click.
Use cases
Financial services: KYC/AML onboarding review
Agents match IDs, addresses, beneficial owners, and sanctions lists, clearing low-risk, complete applications straight through. Cases that hit a watchlist, have unclear documents, or originate from high-risk jurisdictions go to compliance with the full match results, taking onboarding review from days to same-day.
E-commerce & retail: refunds and disputes
Agents read the order, shipping, and complaint, then approve and reply on small-dollar cases that fit policy. Cases above the threshold, suspected abuse, or policy-ambiguous go to a support lead, so most refunds close in minutes.
Manufacturing: invoice three-way match
Agents match the invoice against the PO and goods receipt (PO/MES data). When amount and quantity agree, they post automatically. Price variances, shortages, or over-receipts go to procurement and finance, shortening the month-end close and payment cycle.
Logistics: order and delivery exception triage
Agents watch order status in your TMS/WMS and apply standard handling to exceptions like delays, stockouts, and bad addresses, via reroute, notify, or reissue. Anomalies the rules can't resolve go to a dispatcher, so the floor focuses only on the genuinely hard cases.
Insurance: first-pass claims review
Agents verify policy validity, document completeness, and basic eligibility, auto-approving standard small claims at first pass. Large, suspected-fraud, or incomplete claims go to an adjuster with a review summary, speeding settlement while holding the line on risk.
Delivery cadence
WEEK 1
Decompose the process, define boundaries
Engineers embed and work with your operations team to break down the target process, mapping every decision point, rule, exception, and escalation owner. We define the agent's automation boundaries and confidence thresholds, and pick the first high-volume workflow to ship.
WEEK 2–4
Build and integrate
We wire into ERP, CRM, ticketing, and approval systems, build the agent workflow, audit trail, and escalation rules, then validate decision accuracy by replaying your real historical cases, calibrating to a launch-ready bar.
WEEK 4–6
Canary rollout with human review
Start at 5% of traffic with humans reviewing every agent decision, scaling up against pass criteria. The real-time delivery brief goes live in parallel, so leads always see the auto-completion rate and exception status.
WEEK 6+
Full rollout and continuous tuning
Once stable, we scale to full volume and hand over the runbook and monitoring dashboard. We keep tuning escalation rules and widening the automation scope from accumulated case data, then bring on the next workflow.
Days → Minutes
Cross-department review cycle
80%
Routine cases auto-handled
5% → 100%
Gradual canary rollout
FAQ
What happens when the agent gets it wrong?
The agent only decides on its own when confidence is high and risk is within bounds. Everything else goes to a human. Every case leaves a full audit trail, so you can replay exactly which step failed and why. We catch these errors during the canary phase through human review and recalibrate the rules, scaling traffic only once it's stable, which keeps the blast radius of any error contained.
When something goes wrong, how is accountability assigned?
Every run records the inputs, the data it cited, the rule it applied, and the reasoning, plus whether it auto-completed or who reviewed it. That complete trail stands up to internal audit, compliance, and complaint investigations. The agent doesn't replace accountability. It gives accountability the evidence to stand on.
How much control do we have to give up?
Entirely your call. Which cases auto-complete, which escalate, who reviews, and how wide the automation scope runs are rules you set and adjust anytime. Rollout is gradual, and you can always tighten back to human review in one click. You're never forced into full autonomy at once.
Do we have to rip out our existing systems first?
No. Agents connect to the ERP, CRM, ticketing, approval, and messaging systems you already run via API, reading and writing under your existing permissions. Your staff's interface doesn't change, data doesn't move, and the workflow runs inside your own environment.
How fast until the first workflow is live?
Typically the first high-volume workflow enters canary rollout within 4 to 6 weeks. We embed as FDEs, focus first on the one process with the biggest impact, prove the result, then expand to other workflows, rather than committing to a company-wide rollout up front.
How do you handle data security and compliance?
The workflow runs inside your environment (including your VPC), so data never leaves your boundary, with access governed by your existing permissions. We work to SOC 2 and GDPR requirements, and the full audit trail is itself part of your audit and data-governance posture.
KNOWLEDGE BASE · FURTHER READING
The agentic workflow knowledge base
We've gathered the key knowledge for building supervised-agent workflows here, from Tenten AI's first-hand delivery perspectives to the authoritative sources that shape agent architecture and governance.
Tenten AI whitepapers & perspectives
Authoritative external sources
INSIGHTS · LATEST
The latest on enterprise AI & delivery
First-hand observations and methodology from Tenten AI's delivery floor.
AI workflows,
built into your operations
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