[ Multi-Agent Orchestration ]
We design supervisor and specialist agents that split a large task, pass work through clean handoffs and resolve disagreements before anything reaches your users. Every run has a budget, a trace and a single owner.
Supervisor
Plans the run, assigns agents and enforces budgets.
Research agent
Action agent
Critic agent
Writer agentMulti-agent designs earn their complexity when a task needs different skills, different tools or a second opinion before it ships.
A planner splits the question, research agents gather filings, news and internal notes in parallel, and a writer agent assembles a cited brief that a reviewer agent checks for gaps.
Diligence packs in hours with every claim sourced
Control-specific agents pull evidence from cloud, HR and ticketing systems, map it to each requirement and hand a consolidated package to an auditor agent, similar to the engine we built for Sannos AI.
Audit preparation shifts from weeks to days
Separate agents profile the company, map stakeholders, review past deals and draft an account plan, with the supervisor resolving conflicting signals before a rep sees it.
Reps walk into calls with a complete plan
Inventory, carrier and customer agents each own their system. The supervisor coordinates a fix for delayed or split orders and writes the customer update.
Exceptions closed without three teams in a thread
A redline agent proposes edits, a policy agent checks them against your playbook and a risk agent scores the result, so legal reviews one clean recommendation.
Fewer review cycles per contract
IT, HR and finance agents handle their part of onboarding or offboarding, with handoffs logged so nothing is dropped between departments.
New hires are set up on day one
Most multi-agent failures come from coordination, not from the models. These are the parts we design, test and hand over.
A supervisor agent that decomposes the goal, assigns specialists, tracks progress and decides when the work is complete.
Structured handoff contracts between agents, plus A2A protocol endpoints so agents built on other frameworks or by other vendors can join.
A common state store with scoped read and write access, checkpoints after every step and resumable runs after a failure.
Voting, critic agents and source verification rules for when specialists disagree, with unresolved conflicts escalated to a person.
Per-run token caps, per-agent model tiers and loop detection, so a confused agent cannot burn through your monthly budget.
Independent subtasks fan out and join back, with timeouts and partial-result handling when one branch is slow.
One trace per run that shows which agent did what, the messages between them and where time and tokens were spent.
Tests that score the final output and each handoff, so you can see whether a new agent improves the team or just adds cost.
Agents stay small and focused. Coordination, memory and budgets live in shared layers you can inspect and change.
Where goals arrive and finished work is returned.
Plans the run, assigns agents and enforces budgets.
Narrow agents with their own prompts, tools and model tier.
Memory and tools every agent can reach with scoped access.
Visibility and limits across every agent and run.
We map one workflow, ship a supervisor and one specialist with tracing and budgets, and only add agents when the evals show they improve the result.
Plan an Agent Team PilotThe specialists change by sector. The supervisor, budgets and traces stay the same.
Research, risk and writer agents prepare credit and diligence packs.
Intake, policy and fraud agents each own their step of a claim.
Inventory, carrier and customer agents settle order exceptions together.
Planning and exception agents coordinate across carriers and warehouses.
Support, billing and engineering agents hand off customer issues cleanly.
IT, HR and finance agents run onboarding and offboarding end to end.
More agents means more ways to fail. These controls keep the system predictable as it grows.
5.0Clutch
5.0GoodFirmsI had the pleasure of working with Sajal Tech on a travel website, and it was an incredible experience from start to finish. They demonstrated professionalism, clear understanding of requirements, and excellent communication throughout.
Knowledgeable, efficient, and ahead of schedule. Will hire again.
They completed our project even when the scope changed slightly. They were cooperative when we wanted to add or modify features.
We pick the framework by how much control the workflow needs: explicit graphs for regulated flows, role-based crews for open-ended research.
Indicative starting prices. A free scoping call gives you a fixed quote and an estimated model cost per run.
Option 1
Role map, handoff contracts and a cost model for your workflow before you build.
From
$1,500
Timeline
1 to 2 weeks
Option 2
A supervisor plus specialists on one workflow with tracing and evals.
From
$4,000
Timeline
3 to 5 weeks
Option 3
Many agents across teams, A2A interoperability and full governance.
From
$40,000
Timeline
3 to 6 months
We start with the smallest team that solves the problem and add agents only when the evals show they help.
1 to 2 weeks
Break the process into roles, decide which steps need a separate agent, and define handoff contracts and a cost target per run.
2 to 3 weeks
A supervisor and one or two specialists running on real data in a sandbox, with traces and a first eval suite.
3 to 6 weeks
Add specialists, critic agents and A2A connections one at a time, keeping each only if quality or speed improves.
Ongoing
Monitor handoff failures and spend, swap model tiers per role and retire agents that no longer pull their weight.
Still deciding? A 30-minute call usually answers the rest.
Ask Us DirectlyOften not at first. If one agent with good tools passes your evals, we ship that. We move to multiple agents when the task needs separate skills, parallel work, different permission levels or an independent reviewer.
LangGraph when you need explicit state, checkpoints and auditable paths, which suits regulated or long-running workflows. CrewAI works well for role-based research and content teams, and CrewAI Flows adds deterministic control. We sometimes combine them through A2A.
A2A is an open standard that lets agents discover each other and delegate tasks over JSON-RPC, regardless of framework. It means your agents can work with a partner's agent or a vendor's agent without custom glue code for each pair.
Each agent gets the cheapest model that passes its evals, and every run has token, step and time caps. We report cost per run from traces, and loop detection stops agents that keep calling each other without progress.
We define a resolution rule per workflow: a critic agent, a vote, a source check or escalation to a person. The disagreement and the evidence behind each side are logged, so you can see why the final answer was chosen.
They write to a shared state store with scoped access. A finance agent can see figures it needs while a public-facing writer agent only sees an approved summary. Sensitive fields can be redacted before any handoff.
A two-agent core on real data usually takes 2 to 3 weeks after mapping. A full team with critic agents and integrations takes 6 to 12 weeks in total, depending on the number of systems involved.
Yes. We review your current agent, add a supervisor and tracing, and introduce specialists one at a time with evals that show whether each addition helps. If the existing code is fragile, we start with a short audit.
You own all graphs, prompts, handoff schemas, eval sets and infrastructure code. We document how to add or retire agents so your team can extend the system without us.
Book a free strategy call and leave with a clear plan and estimate. No commitment.