AI Agent Development

Multi-Agent Orchestration

Systems that split a job across specialist agents, each seeing only its own tools, behind one router — with approval tiers and monitoring, in an 8–20 week band.

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Multi-agent orchestration splits a job that would otherwise sit in one large prompt across specialist agents that each see only their own tools, with a router agent sending each request to the right specialist. GG Tech Teknoloji's Multi-Agent Orchestration service first decides whether the job really needs splitting; then it designs the task boundaries, the tool allocation, the human approval points and the cost limits. Our planning band is 8–20 weeks.

GG Tech Teknoloji estimation model — Multi-Agent Orchestration · Last reviewed: 2026-10-14
Typical team2 AI engineers + 1 backend developer
Calendar band8–20 weeks
Phase split25% task decomposition & architecture · 45% development · 20% evals · 10% rollout

What grows the budget

  • Number of agents and interaction pattern
  • Human approval checkpoints
  • Cost & loop control

Signs of underbudgeting

  • Many agents for a single-agent problem
  • No observability

These are GG Tech Teknoloji planning bands, not a quote; starting prices are on each service page. Every project is different, so the bands are deliberately wide; they assume a senior, focused team and typical scope, and your project is estimated individually.

AI Agent Development

Who is it for?

  • Teams whose single-prompt assistant started calling the wrong tools and mixing up jobs as it grew.
  • SaaS products that want to offer very different jobs — onboarding, billing, reporting — through one chat interface.
  • Operations teams handing agents a process that touches several systems and needs a human sign-off at some steps.
  • Product owners holding back from production because they cannot see at which step the agent goes wrong.

When is it the wrong choice?

  • The job turns on a single decision and a handful of tools is enough: one agent is cheaper, faster and easier to monitor.
  • There are no test scenarios or measurements even for one agent yet: build those first, because more agents multiply errors.
  • What you need is answers from company documents: that calls for a well-measured RAG integration, not orchestration.

How do we work?

  1. 1

    One agent or several?

    First we test whether a single agent can do the job. If there are few tools and the flow turns on one decision, multiple agents are needless complexity, and we say so.

  2. 2

    Task decomposition

    The work is split into parts that need different tools and different ways of talking. In Beyond Review, onboarding, billing and competitor analysis became separate agents because they share neither tools nor tone.

  3. 3

    Tool catalogue and allocation

    Each tool goes to exactly one specialist; no agent sees the whole list. An agent that starts confusing its tools is telling you it is time to split.

  4. 4

    Router and context

    The router classifies intent and hands off; it never writes the answer and has no tools of its own. Context survives the hand-off, so the user stays in one conversation.

  5. 5

    Approval tiers and guardrails

    Every action that is visible outside and cannot be undone gets a human approval step; the tier is relaxed as the agent's competence is measured, never the other way round. Loops and token spend get a budget cap.

  6. 6

    Per-agent evals and monitoring

    Each specialist is measured on its own test scenarios; in production, which agent went wrong at which step shows up in its own trace. Changing one specialist's tools does not force retesting the others.

What do we deliver?

Multi-Agent Orchestration

Single-or-multi-agent decision note and task map
Tool catalogue and permission limits per agent
Router, intent classification and hand-off rules
Human approval points and guardrail rules
Per-agent test scenarios and an eval report
Monitoring: agent traces, loop counts, token and tool-call metrics

What sets the timeline and cost?

In our estimation model, multi-agent orchestration sits in an 8–20 week calendar band. Three things move it most: the number of agents and how they interact, the number of points that need human approval, and how tight cost and loop control has to be. Building many agents for a single-agent problem and leaving observability for later are the two early signs that the budget will be wasted. A fixed-scope 4–6 week pilot starts at $25,000 (excl. VAT); the full project is budgeted in a written proposal after task decomposition. The Cost Drivers page explains how to read the bands.

See the cost drivers

Proof from production

6

specialist agents and one router in the same system (Beyond Review)

55

tools split among the specialists; each sees between 6 and 15

0

tools on the router: it only hands off and never writes an answer

Tool counts were taken from the tool lists in the agents' source code (September 2026). The system runs on Google ADK and Gemini.

Read the case study: Beyond Creator

Frequently asked questions

What is a multi-agent system, and how is it different from a single agent?

A multi-agent system is an architecture in which several AI agents share one task. In a single agent every tool and instruction sits in one prompt; as the prompt grows the model loses track of which job it is doing, and as tools multiply it calls the wrong one. In a multi-agent setup each specialist sees only the tools for its own job, and a router hands each request to the right specialist. Every part can then be tested and changed on its own.

How many tools before you should split an agent?

There is no fixed number. The signal is the model starting to confuse tools with similar names. In Beyond Review the 55 tools were never given to one agent: each specialist sees between 6 and 15 of them, and the router has none.

Is a multi-agent system more expensive than a single agent?

Not always. Specialist agents carry only their own tool definitions in context, while one large agent has to carry every definition on every request. The real cost depends on how many agents a request touches and how many loops it runs, which is why we put a budget cap on the agent loop and measure token spend per agent.

Which framework do you build with?

It depends on the context. In Beyond Review the agents run on Google ADK and Gemini, the router classifies intent with DSPy, and session state lives in Redis. For predictable, state-machine style flows, frameworks such as LangGraph are also on the table; the choice comes down to how easily we can observe the system and control its cost.

How do you stop an agent from publishing something wrong?

With approval tiers. Beyond Review's reply flow has three modes — automatic, hybrid and manual — set per business and per star rating. Every generated reply goes through a second model call that checks relevance and whether it promises anything before it is published; a reply that fails three attempts is not published and is left to the owner.

How do you monitor a multi-agent system in production?

Per agent. For every request we record which specialist it went to, how many loops it ran, which tools it called and how many tokens it spent. A failure does not disappear into one long trace: you can see which specialist went wrong at which step and fix only that one.

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Work we delivered with this service

Related reading

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