10decoders
The $2 Million Lesson Nobody Budgeted For
In the last quarter of 2025, a global logistics firm lost roughly $2 million in a single week. Not to a breach, and not to a bad model. A procurement agent, doing exactly what it was built to do, over-ordered inventory ahead of a demand forecast. At the same time, a separate pricing agent, also doing exactly what it was built to do, started liquidating the same surplus stock at a loss to hit a clearance target. Neither agent was faulty. Neither team that built one knew the other agent existed, let alone what it was doing that week. Two reasonable systems, running in the same company, working against each other in real time.
That story is not an outlier anymore, it is close to the median. OutSystems' 2026 State of AI Development Report puts the average enterprise at twelve active agents today, climbing toward twenty by 2027, and Gartner expects 40% of enterprise applications to carry a task-specific agent by the end of this year, up from under 5% in 2025. Every one of those agents got built for a good reason, by a team that had a real problem to solve and a tool that made building an agent easier than filing a ticket with IT. What almost none of them got was a layer above the individual agent that tracks what the fleet is doing as a whole.
This is a different problem from shadow AI. Shadow AI is about tools nobody approved. What breaks a logistics firm's inventory count is the opposite: agents built through the sanctioned channel, by the team that owns the workflow, using the tools IT actually rolled out. The agents are legitimate. The gap is that legitimacy was never the same thing as coordination, and 68% of IBM's surveyed executives now say they expect their AI initiatives to underperform specifically because the pieces do not integrate.
"Every agent your teams ship makes one workflow smarter and the business, taken as a whole, harder to predict."
Where Agent Coordination Actually Breaks
| Failure Point | What It Looks Like | Where It Shows Up | Severity |
|---|---|---|---|
| No shared inventory of active agents | Nobody can list every agent touching a given system or customer | Agent onboarding | Critical |
| No arbitration rule when two agents act on the same resource | Conflicting actions ship at the same time, like the procurement and pricing agents above | Cross-team workflows | Critical |
| Each agent optimizes only its own team's metric | Locally sensible decisions that are collectively expensive | Multi-agent execution | High |
| No shared context or memory layer across agents | Agents repeat the same work or contradict each other's output to the same customer | Customer-facing workflows | High |
| Ownership assigned per team, not per workflow | Nobody is accountable when three agents from three teams touch one process | Incident response | Moderate |
| Compute and API spend untracked across the fleet | Redundant calls between agents inflate cost with no owner to flag it | Budget review | Moderate |
Not sure how many AI agents are already running across your business?
10decoders runs an agent inventory and orchestration assessment for teams scaling agentic AI past a handful of pilots. Most engagements find at least one agent nobody had mapped, and a clear first move to bring the fleet under one view.
Book a Free AI Assessment →Why Orchestration Doesn't Happen on Its Own
Building an agent used to require a project, a budget line, and a review. Now it is often a checkbox inside a SaaS tool a team already had access to, which means agents proliferate faster than any central function can track them, and faster than most companies' procurement process was designed to review. A sales team adds an outreach agent because the CRM vendor shipped one in the last release. A finance team adds a reconciliation agent because the ERP vendor did the same. Neither purchase looked like buying software, so neither went through the review a new system normally would.
Each team also has a genuine incentive to move fast and no incentive to coordinate with a team it does not work with day to day. The sales team's agent is judged on pipeline velocity, not on whether its outreach cadence conflicts with what the support team's agent is telling the same account. That asymmetry, real reward for shipping an individual agent, no reward for the coordination layer above it, is why 68% of executives now expect integration gaps to hold their AI initiatives back even as adoption keeps climbing.
None of this means teams should stop building agents. It means somebody has to own the layer that sits above individual agents: a shared inventory, a shared context store, and a rule for what happens when two agents reach for the same resource at the same time. Almost nobody owns that layer today, which is exactly why 88% of agent pilots still stall before they reach production according to IDC, even as the agents themselves keep getting more capable.
The Three-Stage Climb to an Orchestrated Fleet
Agent by Team
Every team builds and owns its own agent. No shared inventory, no visibility into what another team's agent is doing, no one accountable for the fleet as a whole.
Agent Inventory
A central list of active agents exists and gets reviewed, but there is still no shared context layer or arbitration rule, so conflicts get caught after the fact instead of prevented.
Orchestrated Fleet
Agents share context, a named owner exists for every cross-team workflow, and a clear rule decides what happens when two agents reach for the same resource, before it becomes a $2 million week.
An AI Agent Orchestration Checklist Before You Approve the Next One
"The enterprises that win with agentic AI in 2026 will not be the ones with the most agents. They will be the ones who know what every agent is doing right now."
What to Do This Week
01 Inventory every agent already running, not just the approved pilots
Pull a list from every team, including agents embedded in a SaaS tool that never went through a formal build process. Most companies find their real agent count is higher than what any single dashboard shows, because a feature toggle inside an existing vendor tool rarely gets logged as a new system.
02 Reassign ownership from team to workflow
Find the two or three processes where more than one team's agent touches the same customer, account, or resource, and name one accountable owner for each. That owner does not have to build every agent, but they do have to know when two of them are about to collide.
03 Write one arbitration rule for your highest-risk shared resource
Pick the resource most likely to see two agents act on it at once, inventory, pricing, or a shared customer account, and decide now which agent wins, or who gets paged, when that happens. That single rule would have stopped the $2 million week described above.
04 Put a dollar figure on compute and API spend per agent
Most finance teams can report AI spend by department, not by individual agent. Break it down further for even your top five agents this week, and redundant or duplicated calls between agents usually surface within the first pass.
Let 10decoders Build Your AI Agent Orchestration Layer
We run the agent inventory, map ownership by workflow instead of by team, and design the shared context and arbitration layer your fleet needs before the next conflict costs more than the coordination would have, usually inside a two to three week engagement.