Solution · AI Operations
You have several AI agents. What you don't have is a system.
Once every agent runs on its own, the problem stops being the prompt and becomes the hand-off: who does what, in what order, with what context, and who answers when the chain breaks. That orchestration layer gets built and run. It does not get improvised.
The problem
Five agents that don't talk to each other aren't a system. They're five islands on one bill.
- Two agents touch the same record and the last write wins; nobody knows which of the two was right.
- Work passes from one agent to the next through an improvised webhook, with no input or output contract: when one changes, the one next to it breaks silently.
- Context doesn't travel. The second agent asks the customer again for the data the first one already had, and the customer notices.
- When something goes wrong, nobody knows which agent it went sideways in: there are separate logs per agent and no trace of the full case.
- A load spike fires all five at once against the same ERP, and what blows up isn't the AI: it's the system next door.
Cost of staying the same
Every new agent doesn't add, it multiplies the mess: more surfaces stepping on each other, more routes with no owner, more incidents nobody can attribute. The team ends up spending more time working out which agent did what than on the work the agents were meant to take away. And the ceiling arrives sooner than it looks: not at agent one hundred, but at the fourth.
The solution
We build the orchestration layer —hand-off, order, context and trace— and we run it
- 1Before orchestrating anything, we check it's needed. If the work doesn't genuinely split into chunks that can move forward without checking in at every step, one well-built agent beats five coordinated ones, and we tell you so. Orchestrating complexity that isn't there is the most expensive way to do nothing.
- 2We define the hand-off: what each agent does, what it does NOT do, what it receives and what it returns. Explicit input and output contracts, so swapping one agent doesn't break the one next to it.
- 3We build the engine that decides the order: what runs in parallel, what waits on what, what gets retried and what gets aborted. With shared state, so context travels between steps instead of being asked of the customer again.
- 4We put in concurrency and access control: per-system limits so a spike doesn't take down your ERP, scoped permissions per agent, and locking on the records two agents could step on at the same time.
- 5We close with end-to-end tracing: one case ID that runs through the whole chain, so an incident lands on the exact agent and step, not on opening five dashboards.
- 6We run it: an owner per agent, versioning, repeatable deploys, alerts when a branch degrades, and a regular review of what's redundant in the hand-off.
What changes
What you stop losing
The hand-off between agents stops being an improvised webhook: it becomes a contract with an input, an output and a retry, that you can change without breaking the chain.
Mechanism
Context travels with the case. The customer stops repeating to one agent what they already told another.
Mechanism
An incident stops being an investigation: the end-to-end trace points at the agent and the step where it went sideways, not at the approximate time.
Mechanism
What we measure: cases completed end to end without intervention, failures per hand-off between agents, time to find the cause of an incident, and the load each agent puts on the systems next door.
What we measure
Spec sheet
- Work it removes
- coordinating by hand several agents that step on each other: deciding the order, passing context from one to the next and working out where the chain broke
- Typical setup
- 3–6 weeks
- Input
- a case that needs more than one agent to close end to end
- Output
- the case completed with order, shared context, retries and a single trace running through the whole chain
- Works with
- Agentes construidos sobre cualquier framework (LangGraph, n8n, Temporal, Inngest, código propio)Modelos de OpenAI, Anthropic, Google o self-hostedMCP como conector estándar hacia tus sistemas
- Can connect to
- Your business systems (ERP, CRM, helpdesk, data warehouse)Your current observability, so the trace doesn't live in a separate dashboardYour permissions and identity governance
- What we measure
- cases completed end to end without interventionfailures attributable to a hand-off between agentstime to find the cause of an incidentload each agent generates on the systems next door
- Good fit for
- companies with several agents already in production that step on each other, break each other or don't share context
- Not a fit for
- anyone with a single agent, or who wants several because it sounds modern: if the work doesn't genuinely split, orchestrating only adds cost
Frequently asked questions
No, and the difference matters because it decides what you actually need. Scaling a fleet is a volume problem: how you make adding agent one hundred cost what adding the sixth cost. Orchestrating is a coordination problem, and it shows up much earlier: it hits you at four agents if those four touch the same process, step on each other's records and pass work with no contract. You can have a hundred independent agents and need no orchestration, and have four and need it desperately. If yours is volume, the conversation is scaling a fleet; if yours is agents getting in each other's way, it's this one.
We say it and we stand by it: most office work is handled better by one agent with good tools and continuous context than by a committee of bots passing the buck. That's why the first step of this job is checking whether orchestration is genuinely needed, and why sometimes the delivery is merging three agents into one. We orchestrate when the work genuinely splits —chunks that move forward without checking in at every step, different systems, different rhythms, different permissions— not when a bot org chart looks good on a slide.
No. The layer goes on top of what you already have, whatever framework it runs on. What changes isn't the inside of each agent: it's that there's now an explicit contract for what it receives and what it returns, an engine deciding order and retries, shared state so context travels, and a single trace for the full case. If a redundant agent or two doing the same thing turn up along the way, we flag it —but that's a recommendation backed by data, not a requirement to start.
Want it running in your business?
You’ve pinned the problem. We ship the fix and leave it measured.