Some workflows benefit from separate roles for research, preparation and review. EidenTech can scope multi-agent systems when that separation makes the work easier to evaluate and operate. We first compare the approach with a simpler workflow, then define what each role knows, what it can change and how the application resolves incomplete or contradictory outputs.
How we approach your project
Choose separate roles for a concrete reason
A research role might collect source-backed company information, while a scoring role applies agreed criteria and a review role flags unsupported conclusions. These are different responsibilities with different acceptance conditions. We identify whether separate agents improve those boundaries or merely add more model calls. Straightforward extraction and fixed calculations may belong in ordinary application code. Discovery produces a role map and a comparison with a single-agent or deterministic workflow, so the architecture follows the work rather than a desire to label every component an agent.
Make handoffs explicit and verifiable
Each role receives a defined input and returns a structured result the application can validate. A researcher can distinguish verified information, unavailable information and inference; the next role should not silently treat them as equivalent. We scope shared identifiers, source references and a task history so operators can follow the chain. Handoffs include what happens when a role returns incomplete output or asks for additional evidence. Concurrent tasks need clear ownership of shared records to avoid competing updates or a later step overwriting a reviewer’s decision.
Resolve disagreement without endless conversation
Multiple agents can repeat the same mistake or disagree without providing new evidence. We define who has authority to choose the next step, the permitted retry budget and the conditions that route a case to a person. A reviewer can request a missing source, but it should not trigger an unlimited loop of regenerated opinions. Deterministic validation can reject malformed records before any further discussion. For consequential outputs, the interface shows the relevant evidence and unresolved questions so the business owner can make the decision.
Evaluate the whole system and its individual roles
Testing checks each role’s contract and the final business result. We examine failed handoffs, stale shared data, unavailable tools and cases where confident agreement still produces an unsupported answer. Usage visibility distinguishes useful work from retries and repeated context processing. The release can begin with human approval of the final output and expand only after representative results support that change. Handover documents role instructions, tool access, task state and operating responsibilities. Multi-agent scope is evaluated as a proposed solution; we do not imply that several agents guarantee accuracy.
What we can deliver
We agree the requirements and responsibilities before development begins. Your project scope can include:
- A role and responsibility map comparing multi-agent scope with simpler alternatives.
- Validated handoff contracts, evidence references and a shared task-state design.
- Decision authority, retry budgets and escalation paths for disagreement or missing evidence.
- Role-level and end-to-end evaluation with usage visibility and operating documentation.
Technology choices for your requirements
The stack follows your integrations, operating model, and maintenance needs. These are options to review during discovery, rather than a fixed stack promised for every project.
- Model APIs with structured role outputs
- Application orchestration and validated handoff schemas
- Relational task records, evidence references and bounded execution
- Role-specific supported tools with server-enforced permissions
Planning questions, answered
How do we know whether we need multiple agents?
We look for responsibilities that need different tools, permissions or evaluation criteria. If a single workflow can meet the requirements more clearly, it remains an option. The decision should be based on a useful separation of work rather than an agent count.
Can a reviewer agent approve another agent’s work?
A reviewer can assess evidence and flag issues within agreed rules. That does not replace every human approval or establish that an answer is correct. We define which checks are deterministic, which are model judgements and who approves consequential outcomes.
Can the agents work in parallel?
Independent research or preparation tasks can be scoped for parallel execution. Steps that depend on a confirmed result remain ordered. Shared records need ownership and conflict handling so parallel work does not produce inconsistent updates or duplicate external actions.
How is the cost of a multi-agent workflow controlled?
We define model usage, context size, retry limits and stopping conditions for each role. Representative tasks help estimate operating requirements. Monitoring should show where calls are spent, allowing the team to simplify a role or change its implementation when warranted.
Tell us what needs to work better.
Share your idea, existing workflow, and the outcome you need. We will help shape a practical starting point.
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