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Ontogenesis Technologies

AI & Agentic AI

Multi-agent systems, when one agent genuinely is not enough

Several specialised agents, an explicit coordination pattern, and clear handoffs — used where a single agent's context and authority would be overloaded.

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Multi-agent architecture is often reached for too early. It adds coordination cost, makes failures harder to trace, and multiplies inference spend. We recommend it only when a single agent would need incompatible skills, conflicting permissions, or more context than a model can usefully hold.

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When it is warranted, the design questions are concrete: which agent owns the outcome, how work is divided, what each agent may see, how disagreement resolves, and where a human sits. We make those explicit in the architecture rather than leaving them to emerge from prompts.

We also design for the failure modes specific to these systems — loops, deadlock, context drift between agents, and cascading errors — with step budgets and circuit breakers as standard.

What you get

  • Specialisation without a monolithic prompt
  • Traceable behaviour across the whole workflow
  • Bounded cost despite autonomy

What's included

Supervisor and specialist topologies
A coordinating agent that plans and delegates to narrow specialists, each with its own tools and permissions.
Sequential and parallel pipelines
Work that flows through defined stages, or fans out for independent analysis before synthesis.
Shared state and handoff contracts
Typed structures passed between agents, so a handoff carries data rather than a paraphrase.
Conflict and confidence resolution
Explicit rules for when specialists disagree, including escalation to human judgement.
Loop and cost protection
Step budgets, timeouts, circuit breakers and spend caps enforced at the orchestrator.
End-to-end tracing
One trace across every agent and tool call, so a wrong answer can be explained rather than guessed at.
Where this applies
  • A workflow that spans research, analysis, drafting and checking
  • Tasks needing separate permission boundaries that must not be merged
  • Review patterns where one agent's output should be independently critiqued
Technologies
  • CrewAI
  • LangChain
  • Semantic Kernel
  • ReAct
  • Reflexion
  • Kubernetes
How it works: a supervisor–specialist topology
Supervisor

Owns the outcome

Plans, delegates, resolves disagreement, and enforces the step budget.

  • Retrieval

    Finds and cites the relevant material.

  • Analysis

    Interprets it against rules and history.

  • Action

    Executes the permitted change in the system of record.

  • Review

    Independently checks the result before it is committed.

Shared state

Typed structures passed between agents, so a handoff carries data rather than a paraphrase.

Human-in-the-loop

Approval gates on any consequential action, with the full trace available to the reviewer.

We recommend this pattern only when a single agent would need incompatible skills, conflicting permissions, or more context than a model can usefully hold.

Related capabilities

Ready to talk about Multi-Agent Systems?

Tell us about the process or problem, and we'll tell you plainly whether this is the right starting point.

Review your workflow with usSee our methodology

Or reach us directly at info@ontogenesistechnologies.com · +91-9999101853