AI does the work
Your team stays in control
Claude Code, Codex, or any agent β give it an inbox, tools, memory, and a budget.It does real business work, and every sensitive action waits for your team's approval.
HermesAny service through unified credits
Watch an agent run a real business job, end to end.
See the whole loop in one run: local-business discovery, outreach drafts, a preview report, and a human approval gate before anything is sent.
Your agent alone vs.
your agent + AgentLed.
Your AI agent can reason, code, and plan. AgentLed gives it the working layer: managed agents, email and team channels, any service it needs, durable memory, supervised workflows, approvals, monitoring, and ROI.
| Capability | Agent alone | Agent + AgentLed |
|---|---|---|
| Tools, channels, and credits | Your agent can suggest the workflow, but you still assemble API keys, auth, rate limits, subscriptions, and vendor bills. | Any service your agent needs through one credit pool, one workspace, and one bill. |
| Managed agents and workflows | The agent writes scripts or drafts. Agent identity, inboxes, schedules, retries, state, and handoff remain your problem. | The agent deploys managed agents and supervised workflows with cache, retries, approvals, and managed heartbeats. |
| Business memory | Context windows reset. The agent loses ICP rules, scoring rubrics, prior approvals, and outcomes. | Knowledge Graph stores entities, scores, approvals, decisions, and outcomes across every run. |
| Channels | Drafts, Slack alerts, WhatsApp follow-up, and customer threads stay scattered across tools. | Agent inbox, agent email, Slack/WhatsApp notifications, and customer-facing handoff live in the workspace. |
| Approvals | Approvals happen in chat. No durable record of who approved what or why. | Approval queues pause sensitive actions before sends, CRM updates, publishing, or customer-facing work. |
| Monitoring | Terminal logs. You become the audit trail and reconstruct failures manually. | Run history, step inputs/outputs, exceptions, credit use, and owner actions are traceable. |
| ROI portal | ROI is a spreadsheet you update later, detached from the workflow runs. | Portal shows credits, tokens, hours saved, cost avoided, pipeline influenced, and review outcomes. |
Connect your agent.
Give it a real job.
Connect
Install AgentLed in Claude Code, Codex, or any MCP agent.
Set the goal
Describe the outcome. AgentLed builds and runs the workflow.
Approve & improve
Review sensitive actions. Feedback becomes memory.
Your agent gets an inbox, an email address, and a seat in your channels.
Give agents managed channels for replies, alerts, and handoffs. Every email, Slack alert, and WhatsApp escalation stays attached to the workflow run that created it.
Growth Agent
growth@company.agentled.ai
agent@company.agentled.ai
3 replies need review
Slack
#sales-ops
Daily run summary posted
Agency owner alerts
Qualified reply escalated
Unified agent inbox
LiveFounder reply
Asked for revised pricing after SEO preview
Slack alert
Outbound workflow found 5 high-intent accounts
WhatsApp note
Client approved full GBP report
Approval queue
Send 12 personalized founder follow-ups
Email Β· waits for owner
Update CRM stage for 5 qualified accounts
HubSpot Β· waits for owner
Low-confidence investor fit score
Deal flow Β· waits for owner
Deployment ROI
Hours saved
84
Cost avoided
$12.4K
Pipeline influenced
$38K
Approval rate
91%
Let agents act, but keep the business in control.
Sensitive actions pause before they hit customers or systems of record. Owners approve, exceptions are tracked, and ROI stays visible from the same portal.
Teams are building managed agents with AgentLed.
Examples of managed agent workflows being built and deployed with AgentLed. The agent gets the goal; AgentLed supplies managed agents, workflow runtime, tools, memory, approvals, monitoring, and the ROI portal.
Inovexus β VC Managed Agents
inovexus.comClient goal
βHelp our investment team source, score, and match companies with the right investors or mentors while the system remembers every decision.β
Workflow deployed
Inovexus is deploying managed AI agents with AgentLed to support startup sourcing and investor matching. Agents monitor deal channels, score companies against the investment thesis, recommend relevant investors or mentors, and generate approval-ready reports so the team keeps control while every decision is remembered.
Outcome
Pilot deployment in progress across startup sourcing, thesis-based scoring, investor recommendations, and approval-ready reports.
Agwanet β Agency SEO Workflow
agwanet.comClient goal
βTurn our local SEO consulting offer into a repeatable Google Business Profile lead-gen workflow.β
Workflow deployed
Agwanet used the AgentLed CLI to build and run a Google Business Profile lead-gen workflow for local-business SEO leads. The agent generates a preview report, queues teaser outreach, gates the full report after payment, and creates an upsell path into the agency's SEO services. Agwanet is now connecting AgentLed into Hermes so its own agent can deploy new AI integrations, trigger SEO workflows, and monitor results.
Outcome
First workflow running in one day, with payment-gated reports and an upsell path into agency services.
These are two examples. More clients are building custom AgentLed deployments with connected tools, private data, approval gates, and integrations across their existing stack.
One API key. Any service your agent needs.
No more juggling API accounts. Every integration runs on AgentLed credits, with 300 free credits to start.
| Capability | Credits | What you'd need otherwise |
|---|---|---|
| LinkedIn company enrichment | 50 | |
| Email finding (Hunter) | 5 | |
| AI analysis (Claude/GPT) | 10β30 | |
| Web scraping | 3β10 | |
| Image generation | 30 | |
| Video generation (8s) | 300 | |
| Knowledge Graph storage | 1β2 |
Prioritize tokens for high-ROI work.
Every run is attributed by model, app, workflow step, and agent so teams can allocate monthly-plan credits against hours saved, operating cost avoided, or revenue unlocked.
Plan credits allocated
8,420
Runs
126
Token drivers Β· Current refresh cycle Β· Jun 1-Jul 1, 2026
ROI view: 84 hours saved Β· $12.4K cost avoided Β· $38K pipeline influenced
Models
GPT-5.5
openai
2,380 cr Β· 74 runs
Claude Opus
anthropic
1,840 cr Β· 31 runs
Gemini 3 Pro
940 cr Β· 52 runs
Mistral Small 4
mistral
280 cr Β· 9 runs
Apps with attribution
Profile enrichment
1,260 cr Β· 42 runs
Firecrawl
Web extraction
890 cr Β· 36 runs
Personal follow-up
520 cr Β· 28 runs
KG Memory
Read and update
410 cr Β· 96 runs
Steps
Match ICP
Account fit
1,680 cr Β· 18 runs
Read Signals
Recent context
1,320 cr Β· 42 runs
Write Touch
Personalized
980 cr Β· 34 runs
Save Memory
Next action
620 cr Β· 32 runs
Agents
Warm ICP SEO
Finds best-fit leads
1,480 cr Β· 16 runs
Signal Scout
Reads buying triggers
1,120 cr Β· 24 runs
Content Manager
Writes 1:1 assets
760 cr Β· 18 runs
Reengagement Lead
Remembers next step
520 cr Β· 37 runs
Start with 300 free credits, then allocate credits where ROI is highest.
Agents that remember,
learn, and improve.
Your agent uses workflows behind the scenes β and persistent memory to get smarter over time. Two layers:
Not just automation β a system that gets smarter with every run.
Learn more about Knowledge GraphKnowledge Graph
847 entities Β· 2,340 relationships
Recent Activity
Compound Learning
Investor scoring accuracy improving with each execution
| n8n / Zapier | AgentLed | |
|---|---|---|
| Remembers last run | β | β |
| Cross-workflow memory | β | β |
| Compound scoring | β | β |
| Prediction vs outcome | β | β |
| Learns from results | β | β |
| One API key for any service | β | β |
Every other tool starts from zero.
n8n runs the same workflow with no memory of previous results. Custom scripts need you to build and maintain your own database.
AgentLed's Knowledge Graph stores every insight, score, and outcome automatically. Each run compounds on the last. After 12 runs, our investor scoring went from 62% to 89% accuracy β with zero manual tuning.
See how it worksManaged agents that keep working.
NEW β Agent orchestration
Define the agent, give it email and team channels, connect tools, and set the approval rules. It can run on demand or on a heartbeat, then bring customer-facing actions back to your team before anything sensitive ships.
Deal Sourcing Agent
Human-supervised Β· heartbeat: every 48h
Connected workflows
- β deal-sourcing-specter
- β deal-sourcing-linkedin
- β daily-deal-flow
Agent
Found 8 new deals this week. 2 need your review.
Get started fast
Ready-made jobs.
Customize as you go.
Pre-built deployments for the jobs teams hire agents for most. Pick one, tell your agent what to change, and it adapts the workflow to your business.
FAQ
Questions teams ask before starting
What is AgentLed?
AgentLed is the workspace that turns a capable AI agent into a managed employee. Your agent gets email and team channels, any service it needs through unified credits, persistent memory via a Knowledge Graph, approvals, monitoring, and ROI reporting β from Claude Code, Codex, Hermes, OpenClaw, or any MCP client.
Does AgentLed work with Claude Code, Cursor, or Codex?
Yes. AgentLed is MCP-native. Run npx -y @agentled/mcp-server to connect any MCP-compatible client β Claude Code, Codex, Cursor, or Windsurf β and create, manage, and execute agents and workflows directly from your AI coding environment.
How is this different from using ChatGPT or Claude directly?
Prompt tools have no workflow state, no system integrations, no team visibility, and no calibration. Each conversation starts from zero. AgentLed turns that one-shot interaction into a durable, improving, collaborative workflow.
How is this different from n8n, Make, or Zapier?
Those tools are trigger-action chains without built-in AI reasoning, a knowledge graph, or shared business context. AgentLed orchestrates multi-step workflows where an AI agent reasons, decides, and learns β with integrated model credits, parallelization, and team collaboration.
Do we need engineers to launch workflows?
For most workflows, no. AgentLed is designed for operations teams. For complex environments with custom security requirements, IT review may be needed, but the build itself does not require engineering.
What happens to our data?
Your data is never used to train generalized AI models. Processing is scoped to your account. GDPR-compliant. Full audit trail. Export and data portability options are available.
How much does AgentLed cost?
Start with 300 free credits β enough to connect an agent, run supervised work, and test the tools you need. Pro starts at β¬23.90/month for 2,000 credits, Teams at β¬86.90/month for 7,000 credits with unlimited members, and Enterprise is custom. Credits are shared across your workspace β no per-seat fees.
