Founder operator
Daily company pulse, blocker triage, investor update drafts, and follow-up plans from live business context.
Morning operating briefFounder inbox triageWeekly investor draft
Private agents - verified work
The Agent That Learns With You.
Not a chatbot, not a toy copilot, and not another disposable AI wrapper. Armalo Agent learns your business, runs across tools and channels, completes real work, and proves what it did with a trust record you can inspect.
Paid managed autonomy. No open-source license, no freemium trap.
Private managed agent
Paid activation path
Verifiable run receipts
Trust earned over time
Paid activation
From purchase to proof
Verified
1. Choose
Pick the paid agent role: founder operator, growth, engineering, support, or custom workflow.
2. Connect
Grant scoped access to the tools, budgets, files, and approval gates it needs to run real work.
3. Verify
Review the run receipt, trust signals, artifacts, costs, approvals, and next recommended action.
What Armalo Agent can own
Give it a job, not just a prompt.
The funnel should feel like hiring a capable teammate: give it a domain, connect the right tools, set the risk limits, and expect tangible outputs.
Growth and marketing
Researches accounts, audits funnel leaks, drafts campaigns, and learns which messages actually convert.
Lead researchLanding-page critiqueCampaign follow-up
Engineering execution
Scans repos, ranks fixes, writes implementation plans, prepares patches, and verifies work with tests.
Repo scanTest planPR-ready patch
Operations and customer work
Handles recurring checks, customer replies, vendor nudges, support research, and escalation packets.
SLA monitorCustomer briefEscalation receipt
Proof-first autonomy
A private agent you can inspect, govern, and promote.
A useful agent should get more capable as it works, but capability alone is not enough. Armalo ties learning to receipts, pacts, scores, and approval gates so authority is earned instead of assumed.
Run receipt
Every serious task gets a durable run receipt: request, tool calls, outputs, approvals, costs, skipped steps, and next recommendation.
Governed access
Tool budgets, approval gates, scoped credentials, and audit trails keep the agent useful without handing it the whole company.
Trust score
The agent earns or loses trust as evaluations, outcomes, pacts, corrections, and customer-visible work accumulate.
Recursive self-improvement
Every correction becomes training signal. Failures become ranked repair missions instead of disappearing into chat history.
Harness-grade capability
Tools, channels, environments, skills, and research loops.
Broad agent capability is table stakes. Armalo turns each capability into a proof packet, pact, score, approval policy, and learning writeback so autonomy can be expanded deliberately.
Trainable autonomy, not demo autonomy
The harness should not merely run tools. It should collect trajectories, checkpoint work, delegate safely, compare outcomes, and convert corrections into stronger future agents.
Tenant-scoped tools
Web search, terminal, files, browser automation, vision, image generation, code execution, planning, memory, and model reasoning are useful only when each call has scope, budget, audit, and replay evidence.
Tool call receipt
Customer gateways
WhatsApp, Slack, email, CLI, web chat, and future channels should all enter the same governed session spine, so work can start anywhere and still land with one trace.
Gateway session id
Runtime environments
Local runs, Docker, SSH, sandbox, Modal-style remote execution, and ECS tasks need hardened permissions, stopped-task visibility, and tenant-aware rollback handles.
Runtime evidence packet
Skill memory
Bundled operating skills and newly synthesized skills should carry provenance, use counts, stale-state review, and safe re-injection through ToolRegistry rather than a second skill catalog.
Learning writeback
Research trajectories
Batch research, parallel workers, checkpoints, ShareGPT-style exports, and RL training loops become valuable when trajectories compress into evals, prompts, skills, and promotion gates.
Trajectory to eval
How it starts
From request to proof packet in four moves.
01
Choose the agent
Pick the paid operating role you want Armalo Agent to own first: founder operator, growth, engineering, support, or a custom workflow.
02
Connect scoped tools
Grant only the apps, APIs, files, and budgets the role needs. Sensitive actions can require approval before execution.
03
Run real work
The agent researches, writes, edits, calls tools, creates artifacts, and escalates when authority is missing.
04
Verify and improve
Inspect the receipt, approve next steps, and let evals turn corrections into a better future version.
Why Armalo wins
Generic assistants are helpful. Armalo Agent is accountable.
The buyer does not only need capability. They need confidence that the agent can be trusted with real tools, real customers, and real business context.
| Capability | Chatbot | Generic agent | Armalo Agent |
|---|---|---|---|
| Work execution | Answers and drafts | Can execute tasks | Executes with run receipt and policy |
| Tool access | Manual or ad hoc | Connected apps | Connected apps plus budgets, approval gates, and audit |
| Memory | Conversation memory | Workspace context | Provenance-aware memory and learning writeback |
| Accountability | Trust the output | Review the result | Inspect proof, trust score, pacts, and consequences |
| Improvement loop | Prompt again | Try again | Failures become evals, fixes, and promotion gates |
Operator console
Watch active work, approvals, tool calls, blockers, and output quality from one surface.
Work product
Receive briefs, reports, patches, research, replies, and operating plans instead of raw chat transcripts.
Proof packet
Keep the evidence trail that proves what happened and what still needs human review.
Security and control
Deep capability without blind delegation.
Armalo Agent is built for work that touches systems, customers, code, and money. That means real autonomy must be paired with scoped permissions, review gates, revocation, and receipts.
Approval gates
Require human review before sensitive actions.
Tool budgets
Limit spend, rate, scope, and authority per capability.
Audit trail
Review every meaningful model, tool, memory, and policy step.
Economic accountability
Pacts, trust score, credits, and escrow connect work to consequences.
FAQ
Serious autonomy deserves serious answers.
Is this just another chatbot?
No. Chatbots answer. Armalo Agent is designed as a governed private agent: it can run workflows, use tools, write artifacts, request approvals, and leave proof behind.
Can it act autonomously?
Yes, within the scope you grant. Autonomy expands through explicit tool budgets, approval gates, trust score, pacts, and receipts. High-risk work can stay supervised.
What makes it recursive self-improving?
Runs are evaluated, failures become repair missions, corrections become learning signal, and new versions can be promoted only after evidence improves.
How do I know what it did?
Each meaningful run is framed around a proof packet: request, actions, tool calls, approvals, artifacts, costs, and residual risk.
Bring on the agent that learns while it works.
Start with one scoped role. Inspect every run receipt. Expand autonomy only when the evidence says the agent has earned it.
setup in minutes paid from day one measure every run improve from evals governed by default