Many agents. One reliable outcome.Supervisor + handoff orchestration for production portfolios.
One agent works. Two agents talk. Five agents argue forever unless the orchestration is right. We design the orchestration patterns that make multi-agent systems reliable — supervisor for the plan, parallel sub-agents for the read phase, handoffs for graceful recovery — and ship them with the per-step observability that lets you debug across agent boundaries.
Classifier (Haiku) → specialist agents per category → handoff back to human or system action.
Industries served
IT ServicesEnterprise SoftwareContent + MarketingCustomer Ops
System architecture
How the system is wired
Supervisor + parallel sub-agent pattern
Technology
Multi-agent technology stack
Methodology
Multi-agent delivery methodology
01
Role decomposition
Identify the specialists. Each agent gets one clear job and one scoped tool registry.
02
Orchestration choice
Supervisor for known workflows. Handoffs for recovery-critical paths. Swarm only for genuinely parallel exploration.
03
Integration trace design
Per-agent trace plus an integration trace that surfaces the handoff context. Otherwise multi-agent debugging is guesswork.
04
Eval at every boundary
Each agent has its own eval set. The integration step has an end-to-end eval. Drift in either is visible.
05
Production rollout
Behind feature flags per agent. Parallel run with the existing manual workflow before cutover. Cost and accuracy compared explicitly.
Security & scalability
Multi-agent security & scale
Per-agent scopes
Each agent gets the minimum tool registry it needs. The reviewer cannot deploy. The deployer cannot read PII.
Bounded fan-out
Parallel sub-agent counts are bounded by configuration, not by the supervisor's imagination.
Cross-agent audit
A single audit trail covers the full workflow even when 5 agents touched it.
Integrations
Multi-agent integration surface
Shared MCP servers across all agents in the workflow
Queue-based fan-out for high-throughput pipelines
GitHub Actions / GitLab CI / custom orchestrators
Langfuse for per-agent and integration traces
Business impact
Why multi-agent beats one-big-agent
A single agent loaded with every tool gets worse at every step. Specialists with narrow scopes are more accurate, cheaper, and easier to debug.
4–5×
speedup on read-parallel workflows
~30%
lower cost per completed task vs. one-big-agent
< 90 s
PR pipeline end-to-end on a typical change
Case studies
How recent engagements actually shipped
IT Services · 6 weeks discovery → handoff
PR review pipeline cuts senior-engineer time 4×
Mid-market IT services firm · Ahmedabad · 180 engineers
Problem
Senior engineers were spending 8–12 hours per week each on first-pass PR review across a 6-team monorepo. Junior PRs waited 2+ days for sign-off; velocity stalled; the highest-judgement people were doing the lowest-judgement work.
Solution
A multi-agent CI workflow triggered on every PR open. Three specialist agents run in parallel — a reviewer (Claude Sonnet 4.6) for code-correctness and convention, a security agent for risk patterns, and a test-generator agent for coverage gaps. Outputs are consolidated into a single PR comment within 90 seconds. Humans review the agent's synthesis, not the raw diff.
Claude Sonnet 4.6 (reviewCustom MCP server: GitHub APIGitHub ActionsLangfuse traces
~36 hrs/wk
senior engineer time reclaimed across the team
< 3 days
payback period at loaded-cost rate
4×
review throughput per senior engineer
0
production regressions traced to AI-passed reviews in 90 days
Workshop / Public Build · 1 day · 8 hours hands-on
The Agentic Operating System — workshop build
AIMED · public workshop · ~40 engineers
Problem
Most teams meeting agentic AI for the first time get stuck on one of three blockers: tool design, orchestration choice, and the gap between a working demo and a system that survives Monday morning. The AIMED workshop format compresses the answers into one day of hands-on building.
Solution
A day-long live build of "the Agentic Operating System" — a multi-agent shell with a supervisor (planning, decomposition), handoff agents (parallel reads, sequenced writes), shared tool registry via MCP, and observability wired in from line one. Every attendee leaves with a running shell on their own laptop, the source, and the patterns to extend it.
Claude Sonnet 4.6 + Haiku 4.5 (free Claude Code tier worked)Three MCP servers built from scratch: filesPython supervisor + handoff context passingLangfuse traces from the first agent call
40
engineers shipped a running multi-agent shell on their own laptops
Multi-agent research synthesis — open PoC for swarm vs supervisor
Public R&D · open-source on GitHub
Problem
Every team building multi-agent systems faces the same orchestration question and answers it from intuition, not measurement. "Supervisor is cleaner" vs "swarm is faster" gets stated as fact in a hundred conference talks without a single side-by-side benchmark anyone can reproduce. This PoC builds and measures both, on a task with a defensible ground truth.
Solution
A reproducible benchmark: same task (synthesise a literature review across 12 papers on a given topic), same model, same MCP tool registry, same eval rubric. Three runners — single-agent (baseline), supervisor pattern, swarm pattern — each scored on factuality, citation accuracy, coverage, and cost. Code + eval data + raw runs all open-sourced.
Claude Sonnet 4.6 (all three runners use the same model)Custom MCP servers: paper-fetchThree parallel implementations sharing the same tool registryOpen eval rubric
Bring the workflow you want to automate. We sketch the agent boundaries, the orchestration pattern, and the cost / latency envelope in a 60-minute session.