One agent is useful. Several that cooperate is leverage.
We design how work moves between agents: clean handoffs, shared context, and oversight that spans the whole flow instead of one step of it.
Which process in your operation is really four jobs pretending to be one?
Thirty minutes with the engineer who builds these workflows. No slide deck.
Six ways agents step on each other.
Get several agents cooperating and the whole process flows. Let them work past each other and you have built a new kind of mess.
- 01 Two agents given overlapping jobs, both half doing it
- 02 Context lost at a handoff, so the next agent works from a guess
- 03 One agent knows something and never passes it on
- 04 Nobody defined when the job is finished, so it loops
- 05 A bad output accepted downstream because nothing verified it
- 06 A person can see one agent at a time and never the whole flow
What goes into XtractSol orchestration
Each orchestrated workflow is built around your process, your systems and your oversight rules. Start by connecting two agents, or coordinate many across a full operation.
Split the work the right way
We map how a process should divide across agents so each does what it is good at and the whole flow still makes sense end to end.
Work that moves, not waits
Clean routing so a task passes to the right agent at the right time, with the handoff verified rather than assumed.
Everyone working from current facts
Context and job state managed centrally, so no agent proceeds on information another one has already superseded.
The whole flow, not one agent
Visibility and control across the entire process, with checkpoints where a person can review, approve or step in.
Connected across the process
The orchestrated flow reads and updates information in the systems your process actually touches, from one end to the other.
You can see what happened
Monitoring and safeguards across the flow, so issues surface early and you get the full trace of which agent did what, on what information.
Four agents, and the thing they all read from
The agents are the easy part. What decides whether a multi-agent system holds together is what sits in the middle of it, and whether anybody checks the work as it changes hands.
Each of these four does one job. None of them keeps its own private copy of the file, because two agents working from two versions of the truth is the failure the Berkeley research puts at more than a third of all cases. There is one shared state and every agent reads from it.
Oversight spans the whole thing rather than one box in it, and the checkpoints sit where you put them. A step that cannot complete stops there and waits for a person, which is the difference between one mistake and four.
Reader
Pulls what the process needs out of the incoming document.
Checker
Tests it against your rules and flags what does not fit.
Follow-up
Chases what is still open until it lands or escalates.
Updater
Writes the approved result into your systems of record.
Oversight that spans the process, not one step of it.
You see where every job sits across the whole flow, and you can step in at any point in it.
Split
The process divided into jobs, each with defined boundaries.
SystemRoute
Work handed to the right agent, with the context it needs.
SystemVerify
Each handoff checked before the next agent picks it up.
SystemReview
At your checkpoints, a person approves or redirects the job.
Your teamHold
A failing step stops there, with its state intact, rather than passing bad work on.
SystemResolve
Held jobs come to a person with the full trace attached.
Your teamAdding agents adds joins, and the joins are where these systems break
Berkeley researchers built the first systematic taxonomy of multi-agent failures from more than 1,600 annotated traces across seven popular frameworks. Their finding was blunt: performance gains from multi-agent setups are often minimal, and the failures cluster in specification, coordination and verification rather than in the models. Their conclusion was that robustness needs better orchestration, not bigger models.
Which is why our first question is whether your process genuinely needs several agents. If one can hold the whole task in view, more agents usually buy you coordination failures instead of capability. When the answer is yes, we design the boundaries precisely, keep shared state authoritative, and verify at every handoff. Then we start with one slice of the process and expand as it proves out.
An unverified handoff is how one agent's small mistake becomes four agents' confident work.
Coordinated across the tools the process touches
An orchestrated flow is only as useful as its reach. We connect it across the systems your process already runs through.
Integration depth varies by system and version. We confirm exactly what is supported for your setup during the workflow audit, including anything that cannot be connected.
Built to hold under load
- ✓ Each agent's scope and limits defined before the build starts
- ✓ One authoritative shared state, not copies drifting apart
- ✓ Handoffs verified, and failing steps held rather than passed on
- ✓ Full trace of which agent acted, when, and on what information
A ladder, not a leap
We learn how your process runs, start where the value is clearest, and add coordination in deliberate stages.
Discovery call
The multi-step process you want to smooth out, and where the handoffs cost you most.
Workflow audit
The process mapped end to end, with the split across agents and the oversight points defined.
Build and test
Each agent tuned on your real cases, then tested together before anyone relies on the flow.
Deploy a slice
We start with a focused part of the process, prove it under real volume, then widen it.
We give you a timeline for your own build after the workflow audit, once we have seen the process and the systems it runs through.
When do I actually need multiple agents instead of one?
Less often than the industry suggests. Splitting work across agents helps when a process is genuinely a chain of different jobs, each needing different context, tools or permissions. If one agent can hold the whole task in view, adding more usually adds coordination failures rather than capability. We will tell you during the audit which case you are in.
How do you keep a multi-agent workflow reliable?
By treating the joins as the real work. Berkeley researchers analysing over 1,600 traces across seven multi-agent frameworks found that roughly 42% of failures came from specification and system design and 37% from agents misaligning with each other. So we define each agent's job and boundaries precisely, manage shared state so nobody works from stale information, and verify output at each handoff rather than only at the end.
What happens when one agent in the chain fails?
The flow stops at that step rather than passing bad work downstream. A failed handoff is retried, and if it keeps failing the job is held with its full state intact and routed to a person. The alternative, letting the next agent proceed on a broken input, is how a single error becomes four.
Can we still oversee the process?
Yes, and oversight spans the whole workflow rather than one agent at a time. You see where every job sits across the flow, and checkpoints let a person review, approve or step in wherever it matters. You also get the full trace of which agent did what and on what information.
Does running several agents cost more than one?
It can, because coordination adds calls and passes context around repeatedly. We route each agent to the cheapest model that clears its bar and keep shared state compact rather than resending everything to everyone. If the coordination overhead outweighs the benefit for your process, that is an argument for fewer agents, and we will say so.
What does it cost?
A build fee for the first coordinated slice plus a monthly fee to run and maintain it. Model usage is billed at cost and shown separately. You get a firm number after the workflow audit, before you commit to anything.
Ready to get started?
Book a free discovery call and we will map how agentic AI can fit your workflows.