Autonomousagentsthatrun24/7
Goal-driven agents that plan, use tools and act across your systems — automating whole workflows, not single tasks.
/ the shift
yesterday · copilots
A copilot assists one person, one prompt at a time.
The human still does the work — every step needs someone in the driver’s seat.
/ how an agent thinks
The autonomous loop
Perceive
Reads context from your systems, documents and live events — the full picture, instantly.
Reason
Plans the steps, weighs options and picks the right tool for each one.
Act
Executes across your APIs and apps — completing real work, not just suggesting it.
Observe
Checks the result, learns from it, and loops until the goal is genuinely done.
/ where agents win
Built for high-volume judgement work
Support triage
Classify, draft and resolve tickets end-to-end — escalating only the exceptions.
Back-office ops
Invoice, onboarding and data-entry workflows run themselves, 24/7.
Research & analysis
Gather, synthesise and brief — turning hours of digging into minutes.
Procurement
Source, compare and draft POs against your rules, with approvals built in.
/ the production gap
A demo agent works once.
A production agent works ten thousand times — or fails silently.
Most teams ship an impressive pilot, then hit the wall — the same wall the whole industry is hitting.
of agentic AI projects will be cancelled by 2027 — cost, unclear value, or weak risk controls.
Gartner, 2025
Runaway cost
Token, tool and retry spend balloons 2–3× past the estimate — with no budget in the loop to stop it.
Unproven value
No traces, no evals — so no one can prove the agent works, or notice the day it quietly stops.
Weak risk controls
One wrong action — unscoped, unlogged, irreversible — and the trust (and the project) is gone.
Every one of these is an operations problem — not a model problem. The fix has a name: AgentOps.
/ agentops
Every agent, under a control plane.
AgentOps is the operational layer that turns a clever prototype into a fleet you can trust in production — observed, governed, evaluated and improved, continuously.
Observe
Every step, tool call and token — traced and fully replayable. A silent failure is never silent again.
Langfuse · Arize · AgentOps
Govern
Scoped permissions, policy checks and budget caps, with a human-in-the-loop on anything consequential.
guardrails · approval gates · audit log
Evaluate
Continuous evals and regression suites — quality scored before and after every deploy, so drift never reaches your users.
golden sets · LLM-as-judge · CI
Improve
Versioned prompts, tools and policies — production signal loops back in, so agents get measurably better, safely.
versioning · feedback loops
faster process cycles
AMDIM engagements
fewer bottlenecks
Industry benchmark
governed, autonomous operation
by design
/ the agent ROI
Turn up the agents.
Watch the math.
Set the sliders to your reality — the saving is computed live, with the formula in plain sight.
Annual cost of full manual handling
You keep / year
$554.4KHours of manual handling / year
10,080 hrs hrs automatedTeam tied to this workflow
5.3 FTEs freed3 agents on this workflow ≈ $554.4K/yr and 5.3 FTEs freed.
/ the depth
Beyond the basics —
the full toolbox
5 capabilities, each backed by a real toolbox — assisted to autonomous. A taste below; the full library runs deep.
techniques · 10 disciplines
Reason & plan
break the goal into the right steps
Orchestrate
route work across steps and agents
Remember
carry context across steps and sessions
Evaluate & trust
prove it works — every step, not just the answer
Govern & control
the AgentOps control plane for autonomy
/ your industry
The hard problems
that actually pay
Every sector has a handful of problems that are genuinely hard — and genuinely worth it. Pick your industry for a taste; the full set lives on the industries hub.
Manufacturing
why it’s hard — Defects are rare events on fast lines; models run at the edge in real time, with near-zero tolerance for a missed fault.
$50B/yr lost to unplanned downtime (Deloitte); AI-in-manufacturing ~35% CAGR to 2030 (Grand View).
Maintenance-workflow agents (diagnose→plan→dispatch)
On an alarm, an agent reads manuals and incident logs, ranks likely causes, drafts the work order, checks parts and schedules the crew — a planner approves the job.
Your best maintenance engineer retires next month; the agent runs the whole triage→work-order→dispatch loop from tribal knowledge, with a human on the release.
evidenceCopilots + RCA cut MTTR 30–40% in analogous ops (McKinsey)
Quality-event triage & CAPA agents
On a quality event, an agent gathers the genealogy, runs root-cause analysis, drafts the CAPA and updates work instructions — a quality engineer approves closure.
A quality escape becomes a recall if the CAPA drags; the agent runs investigate→root-cause→CAPA→document and a quality engineer signs every closure.
Supplier RFQ & negotiation agents
An agent issues RFQs, compares quotes on total cost, negotiates within guardrails and drafts the PO — a buyer approves any award or off-policy term.
Manual sourcing leaves savings on the table; the agent runs RFQ→compare→negotiate→draft within limits, escalating out-of-policy deals to a human buyer.
/ why it pays
Outcomes you can measure
autonomous operation
Workflow automation
Agents that orchestrate multi-step processes across your tools and APIs.
faster cycles
Faster cycle times
Whole workflows compressed from days to minutes with agents in the loop.
fewer bottlenecks
Fewer bottlenecks
Routine decisions handled instantly; humans focus on the exceptions.
AMDIM engagements · Industry benchmark · by design — verified, not invented.
/ what we deliver
End-to-end, not half-built.
/ before you commit
Fair questions
Agents plan and act across your systems to finish whole workflows — but with scoped permissions and human-in-the-loop checkpoints on anything consequential. You decide how much rope.
See where you stand in 5 minutes.
Take the Agent Readiness Scorecard — a tailored score and the fastest path to impact.