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Solutions · Agentic Workflows
Agentic Workflows · multi-step, tool-using AI

Agentic workflows — autonomous AI inside your stack, with tools, memory, and approval gates

Agentic workflows go beyond single-shot LLM calls. Multi-step planning, tool use, shared memory across agents, approval gates for high-stakes actions, and proper observability. We build them on n8n + LangChain or custom Python with Temporal for orchestration. Real production AI that does work, not chatbots that talk about doing it.

Multi-agent
planner + executor + verifier
$0.05–0.50
cost per execution
Day 1
observability built in
agents.dgcore — Multi-agent workflow
LIVE
▸ Planner → executors → verifier · with tools
PlannerExecutorExecutorVerifier
Tools:CRMCalendarEmailSlack
Agents in run
4
Cost / execution
$0.18
Certified on the platforms you already use80+ builds shipped
GoHighLevel
HubSpot
n8n
Make.com
Zapier
Klaviyo
Airtable
▸ Our verdict on Agentic Workflows

Agentic workflows are the right tool when the work involves (1) multi-step reasoning, (2) tool use beyond a single API call, and (3) state that has to persist across steps. They are overkill for single-shot generation tasks (use a simple LLM call). They fail without proper observability — you cannot debug what you cannot see. We build observability in from day 1.

What we deliver

What our agentic workflow engagements cover

Standard scope. Custom scope available on the audit.

🧠

Multi-agent architecture

Planner agent breaks the goal into steps, executor agents do the work, verifier agent checks output. Shared memory keeps state across the whole run.

⌬

Tool integration

CRM, calendar, email, Slack, internal APIs, vector stores, web search. Agents call tools with proper retry-with-backoff and fallback routing.

🛡

Approval gates

High-stakes actions (sending external email, processing refunds, posting to public channels) gate behind human approval. Configurable per workflow.

📊

Observability + eval

Per-step traces in LangSmith or our custom dashboard. Token cost tracking. Eval suite catches regressions. The boring infrastructure that makes agents trustworthy.

Engagement model

From audit to live Agentic Workflow build in 4 steps

Same engagement shape as every digicore101 build. Predictable timeline, predictable cost, no scope creep.

017 days

AI Audit

60-min strategy session, stack map, leak analysis, costed roadmap. Vendor-neutral — yours to keep.

  • ·Architecture diagram
  • ·Build sequence
  • ·Cost + timeline lock
025–10 days

Architecture

Agentic Workflow schema, automations on paper, integration map, AI agent personas.

  • ·Approved schema
  • ·Sign-off on flows
  • ·Migration plan if applicable
032–6 weeks

Build & Deploy

Weekly demos, staged rollout, full handoff documentation. You own everything.

  • ·Live system
  • ·Loom walkthroughs
  • ·Team training session
04Ongoing

Train & Support

Retainer keeps the Agentic Workflow stack tuned, monitored, and improving — not just running.

  • ·Slack channel
  • ·Weekly tune cycle
  • ·Monthly reporting
The math

Single LLM call vs agentic workflow · per task type

Single LLM calls handle single-shot tasks. Agentic workflows handle multi-step work that requires tools, memory, and verification. Picking the wrong shape wastes money or fails entirely.

✓Single LLM call: $0.001–0.01 per call · single-shot tasks · no tools
✓Simple agent (1 tool): $0.01–0.05 per execution · single tool use
✓Multi-agent workflow: $0.05–0.50 per execution · planner + tools + verify
✓Right architecture · right cost · right reliability
Architecture by task complexity
Single LLM call$0.005
Single-shot · no tools · no memory
Single-tool agent$0.03
One tool · simple state
Multi-agent workflow$0.25
Planner + executor + verifier · multi-tool
The math
Right shape wins
pick architecture before LLM model
Agentic Workflows vs Digicore AI

How Agentic Workflows compares to Digicore AI

A side-by-side on what each platform actually does. Vendor-neutral — we work in both.

CapabilityAgentic WorkflowsDigicore AI
ArchitectureSingle LLM callMulti-agent · planner + executor + verifier
Tool useFunction calling for one toolMulti-tool with retry + fallback routing
MemoryNone or short contextShared memory across agents + persistence
Approval gatesYolo executeConfigurable gates for high-stakes actions
ObservabilityConsole logsPer-step traces · token tracking · accuracy metrics
Best forDemo workflowsProduction ops · finance · sales · CS
Recent agentic workflow work

How real teams used this

Names anonymized where requested.

Sales ops

B2B · pipeline-hygiene agent · weekly reviews

Multi-agent workflow audits the pipeline weekly: stale deals, missing data, ghost risk. Plans next-action per deal, drafts follow-ups, schedules reviews. Reps get a clean queue.

Pipeline auditWeekly runMulti-agent
Content

Agency · content engine agent · 100+ variants/wk

Planner agent breaks each brief into 10 variant strategies, executor agents draft each, verifier agent checks brand fit. 100+ variants/wk with founder approval gate.

100+/wkApproval gateBrand-checked
Finance

SaaS · refund + dispute review agent

Reads refund requests, pulls customer history, evaluates against policy, drafts response, escalates edge cases. Founder approves before send.

Refund reviewPolicy-awareApproval gate
CS

B2B · proactive customer-success agent

Multi-agent monitors usage, predicts churn risk, drafts tailored re-engagement plays, schedules CS check-ins. Reduced involuntary churn 18% in Q1.

PredictiveRe-engagement−18% churn
When this fits

Honest scope — and who shouldn't engage

Agentic workflows are powerful when scope and observability are tight.

✓ Engage when
  • Multi-step work with state
    When a single LLM call cannot finish the job — multi-step + tools + memory.
  • Tools beyond a single API call
    CRM lookup + email draft + calendar book + Slack post — agentic shines here.
  • High-stakes actions need approval
    Configurable approval gates so the agent does not yolo-execute risky actions.
  • You have observability budget
    Production agents fail. You need traces to debug. Allocate budget for it.
✗ Don't engage when
  • Single-shot generation tasks
    A simple LLM call with the right prompt wins. No agent overhead.
  • Workflows that must be 99.99% reliable
    High-determinism workflows belong in code, not agents. Use agents for graceful-failure use cases.
  • No observability commitment
    Agents without traces are unmaintainable. Skip the build.
Pricing depends on scope

Every Agentic Workflows build is a different shape.

We don't quote off a feature checklist — we quote off your stack, your bottleneck, and the build phases that actually move revenue. The audit is the front door: free, 7-day costed roadmap, vendor-neutral.

FAQ

Questions before we start

Are agentic workflows ready for production?+
Yes — when built right. Frontier models (GPT-4o, Claude Sonnet 4.6) plus proper observability, retry logic, and approval gates produce reliable workflows in narrow domains. They fail at "do anything" agents. Scope tightly and they work.
What is your stack?+
Default: n8n + LangChain for medium-complexity. Custom Python + Temporal for high-volume or stateful workflows. We pick the stack based on workflow shape, not based on what we want to sell.
How do you debug a misbehaving agent?+
LangSmith or our internal trace UI logs every step (prompt, tool call, tool response, model response, cost, latency). When an agent goes off the rails, we replay the trace, find the failure point, and fix it. Without traces, debugging is impossible.
What about cost?+
Multi-agent workflows can run expensive at scale. We design with cost in mind: cheap models for classification + routing, expensive models only for complex reasoning. Typical production agent runs $0.05–0.50 per execution depending on complexity.
What does it cost to build?+
Full Build $1,997+ (multi-agent with observability + approval gates). Complex production workflows $4,997–$12,997. Retainer $797+/mo for tuning, eval, and new workflows.
Keep exploring

Where Agentic Workflows fits in the bigger picture

Most engagements layer 2–3 platforms with a service shape. These pages map the surrounding territory.

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Industries we serve
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Ready when you are

Ready to scope your Agentic Workflows build?

Book the free AI System Audit. We map your stack, find the leaks, and deliver a build roadmap in 7 days. Vendor-neutral.