Building GentArk has been one of those journeys that keeps challenging me and my understanding of AI platforms, especially around orchestration.
AI team orchestration is not a solved problem. It is an active one. While we now have access to powerful models, agent frameworks, routing mechanisms, memory layers, and workflow tooling, the hard question is how to make all this work automatically, in a vertically agnostic way, without relying on rigid templates or domain‑specific adapters.
Defining agents, assigning roles, wiring orchestration logic, and getting responses from agents is achievable today. That part is challenging but I was able to build it in GentArk.
The real challenge begins after the agents respond.
This post focuses on that stage: the solution build stage. The part that rarely gets attention in diagrams but ultimately determines whether an orchestration system produces something usable or just a collection of plausible outputs.
To keep this grounded I want to share what I see while developing GentArk, especially when you try to assemble agent outputs into a coherent, reliable solution.
The Illusion of Progress: When Agents Start Responding
There is a familiar phase in most AI projects where momentum feels high. Agents are defined, Roles are clear e.g., Research, planning, validation, execution, critique or review, etc.
You run the system and get responses from agents.
At that point, it feels like progress. The system is active. Information is flowing. Tasks are being processed. Logs look healthy. Tokens are being consumed.
But this phase can be misleading.
Agent responses, on their own, are not a solution. They are inputs. Raw material that still needs to be interpreted, aligned, and assembled.
Why Response Quality Alone Is Not Enough
Modern models can produce strong answers. Many agent responses are individually correct, thoughtful, and actionable. The challenge is not response quality.
The challenge is that correctness in isolation does not guarantee correctness in combination.
A system can receive multiple high‑quality responses and still fail to produce a usable outcome if those responses are not integrated properly.
In GentArk, agents operate within the same conversation and shared context, with clearly scoped responsibilities. Tasks are not duplicated across agents, and outputs are never concatenated into a solution by default. Even with these constraints, assembling a solution remains non‑trivial.
Because the hard part is not what each agent says, but how everything fits together.
The Build‑Solution Stage: Where the Real Challenge Is
The build‑solution stage starts once agent responses are available and continues until there is something that can actually be executed, validated, or delivered.
This stage is responsible for:
- Interpreting agent outputs
- Aligning them with the original intent
- Resolving overlaps or gaps
- Validating assumptions
- Applying corrections
- Iterating where necessary
This is not a single step. It is a controlled process.
This is also where orchestration systems are truly tested.
Integration Is the Real Work
Integration is not something that happens at the end of a run.
It starts with the first agent responses and continues throughout the entire execution until a solution is built. Early outputs influence how later responses should be interpreted, constrained, or adjusted. As new information arrives, previously collected outputs may need to be re‑evaluated.
Over time, it becomes clear that integration logic often grows more complex than the agents themselves.
And this logic cannot be generic.
It must adapt to the problem type, the expectations of the output, and the execution context. Doing this in a way that is vertically agnostic, fully automatic, and not dependent on predefined templates and workflows is one of the hardest parts of the system.
Validation Is a Continuous Process
Validation is often described as a final step. In practice, it is a loop that runs throughout the solution build.
Validation applies to:
- Inputs
- Agent interpretations
- Intermediate representations
- The assembled solution
- Execution results
Issues discovered during validation often require stepping back, adjusting assumptions, or re‑running parts of the system.
This is where orchestration shifts from simple workflows to something closer to a control system.
Review and Fix: Where Costs Start to Matter
The review‑fix cycle is the point where costs begin to surface.
Each review may trigger fixes. Each fix may require more calls, more context, or partial re‑execution. Over time, token usage and compute costs can quietly creep up.
This is not inherently a problem, but it must be managed intentionally.
Left unchecked, this cycle can become the dominant cost driver in large solution builds.
The Limits of Naive Pipelines
Linear pipelines work for simple cases.
- Ask agents
- Collect responses
- Assemble output
As complexity increases, this approach quickly shows its limits.
Small changes in upstream prompts or constraints can have wide‑reaching effects downstream if the integration layer is not designed to absorb and manage those changes.
This is why orchestration needs to be treated as a dynamic system rather than a static workflow.
Orchestration vs Coordination in AI
Coordination in AI systems is about sequencing and logistics. It ensures agents run in the correct order, receive the right inputs, and pass outputs along the chain. This is similar to coordination in traditional projects: scheduling work and making sure tasks move forward.
Orchestration goes further.
Orchestration handles alignment, synthesis, and meaning. In real‑world terms, coordination gets people into the room. Orchestration ensures they are working toward the same outcome, resolving differences, adapting plans, and producing something usable.
In AI systems, you can have perfect coordination and still fail if orchestration is weak.
Why This Determines System Value
A system can have strong agents, clean prompts, efficient routing, and fast execution and still produce inconsistent or unusable results.
When that happens, the issue is not model capability. It is system design.
The quality of integration, validation, and the review‑fix cycle ultimately determines whether an orchestration system delivers real value.
What I’m Learning While Building GentArk
A few practical takeaways so far:
- Agent outputs should be treated as inputs, not answers
- Integration deserves a higher design effort then prompting
- Validation needs to loop by design
- Review‑fix cycles should be explicit and measurable
- Recovery matters more than perfection
- Integration, review, and fix is the hardest thing and most costly
These are not theoretical insights. They come from building, testing, and refining GentArk.
Closing Thoughts
There has been real progress.
Solution building inside GentArk is working well, particularly for small and medium‑sized projects (due to budget constraint). The integration and validation mechanisms are producing coherent, reliable results, and the system behaves predictably under controlled complexity.
As projects scale, new constraints appear. Large solution builds can run into limits around (budget) the number of calls, token budgets, latency, and operational cost. At that point, the question shifts from whether something can be built to whether it makes sense to build it that way.
This is where cost, alternative approaches, and return on investment start to matter.
AI orchestration is not about pushing systems to extremes for the sake of it. It is about making informed trade‑offs and deploying automation where it creates real leverage.
The capability is there. The focus now is efficiency, sustainability, and value.
That is the direction GentArk is moving in, and it is proving to be the right one.
