Building backend systems that scale requires more than just throwing more servers at a problem. It requires thoughtful architecture, clear abstractions, and a deep understanding of your constraints.
\nBefore you start designing anything, understand your constraints:
\nThese constraints determine everything about your architecture.
\nKeep your application servers stateless. This allows you to:
\nState should live in dedicated systems (databases, caches) designed for that purpose.
\nDifferent components have different scaling characteristics:
\nSeparate these concerns so you can scale each independently.
\nCaching is your best friend for scalability. But it's also a common source of complexity.
\nMost performance problems are database problems.
\nPush slow work off the critical path:
\nConsider an e-commerce platform handling millions of users:
\nThe Problem: Product catalog requests are slow during peak traffic.
\nThe Solution:
\nResult: Response times drop from 200ms to 10ms.
\nYou can't optimize what you can't measure. Instrument everything:
\nUse these metrics to identify bottlenecks.
\nScalability is not a feature you add at the end. It's a consequence of good design decisions made from the start. Focus on:
\nStart simple. Scale when you need to. Design for the scale you actually have, not the scale you might have.
", "url": "https://surajsrivastav.com/blog/designing-scalable-backend-systems/", "title": "Designing Scalable Backend Systems", "summary": "Building backend systems that scale requires more than just throwing more servers at a problem. It requires thoughtful architecture, clear abstractions, and a deep understanding of your constraints.", "date_modified": "2026-05-01T00:00:00.000Z", "author": { "name": "Suraj Srivastav", "url": "https://github.com/surajsrivastav" }, "tags": [ "Systems", "Architecture" ] }, { "id": "https://surajsrivastav.com/blog/ai-agents-production-workflows/", "content_html": "AI agents are moving from research labs to production systems. But deploying agents at scale is fundamentally different from deploying traditional applications. Let me share what we've learned.
\nAn AI agent is a system that can:
\nUnlike traditional chatbots that respond to user input, agents can:
\nAgents introduce new reliability challenges:
\nTraditional code is deterministic. Feed the same input, get the same output. Agents are not.
\nSolution: Design for variability
\nLLMs can confidently generate incorrect information.
\nSolution: Grounding and verification
\nRunning agents at scale gets expensive fast.
\nSolution: Efficient agent design
\nHere's a pattern that works well in production:
\n[User Request]
↓
[Agent Planner] → [Tool Calls] → [Tool Execution]
↓ ↓
←────────── [Reflection] ←─────
↓
[Verification] → Pass? → [Action]
↓
Fail? → [Recovery Strategy]
Planner: The agent plans what to do. Be explicit about constraints.
\nTools: Agents execute via tools. Make tools atomic and well-defined.
\nReflection: After execution, agents reflect on results. This improves decisions.
\nVerification: Always verify critical decisions. Don't blindly trust agent output.
\nRecovery: When things fail, have explicit recovery strategies.
\nWe built an agent to handle customer support tickets:
\nWhat it does:
\nHow we made it reliable:
\nResults:
\nAgents call tools (functions, APIs) to accomplish tasks.
\n# Tools the agent can call
tools = [
lookup_customer,
query_database,
send_email,
escalate_to_human
]
# Agent decides which tools to use
agent = create_agent(tools=tools, constraints=[
\"Do not handle payments\",
\"Always escalate legal issues\",
\"Verify before sending emails\"
])
Agents orchestrate workflows where order matters.
\nworkflow = [
{\"step\": \"validate_input\", \"agent\": input_validator},
{\"step\": \"process_request\", \"agent\": processor},
{\"step\": \"verify_output\", \"agent\": verifier},
{\"step\": \"execute_action\", \"agent\": executor},
]
One agent delegates to specialized agents.
\n[Manager Agent]
├─ [Analyst Agent] - analyzes data
├─ [Writer Agent] - writes output
└─ [Reviewer Agent] - reviews quality
You need different metrics for agents:
\nAgents will become standard infrastructure for many workflows. But they require:
\nThe key is treating agents as tools that augment humans, not replace them.
\nStart small. Deploy agents for low-risk, high-value tasks. Learn from production data. Scale gradually.
", "url": "https://surajsrivastav.com/blog/ai-agents-production-workflows/", "title": "AI Agents in Production Workflows", "summary": "AI agents are moving from research labs to production systems. But deploying agents at scale is fundamentally different from deploying traditional applications. Let me share what we've learned.", "date_modified": "2026-04-28T00:00:00.000Z", "author": { "name": "Suraj Srivastav", "url": "https://github.com/surajsrivastav" }, "tags": [ "AI", "Systems" ] }, { "id": "https://surajsrivastav.com/blog/engineering-leadership-at-scale/", "content_html": "Scaling an engineering organization is different from building a small team. The skills that made you successful as an engineer or small team leader don't automatically work at scale.
\nChallenge: You can no longer code your way out of problems.
\nWhat changes:
\nHow to succeed:
\nChallenge: You can't have 1-on-1s with everyone. Systems become critical.
\nWhat changes:
\nHow to succeed:
\nChallenge: You're now playing a different game. Strategy matters more than tactics.
\nWhat changes:
\nHow to succeed:
\nAs teams grow, people need clarity on:
\nThis clarity needs to be documented and reinforced constantly.
\nAt small scale, heroics work. At large scale, they break:
\nBuild systems that work without heroes.
\nMicromanaging doesn't scale. You need:
\nPeople perform best when they have autonomy.
\nWith 50+ people, you can't have everyone in every meeting:
\nAsync is not the default at small scale. It must be intentional.
\nHow you organize directly impacts:
\nToo many layers:
\nUnclear responsibilities:
\nChurn-driven reorganization:
\nClear ownership:
\nLimited hierarchy:
\nCross-functional alignment:
\nDon't create specialized teams (DX team, ops team) until you need them. Generalists are better at small scale.
\nProcesses slow things down. Only add processes when:
\nIf you hire 20 people in a quarter:
\nHire at a sustainable pace. Plan 6-12 months ahead.
\nCulture is how you get people to do the right thing when you're not in the room. At scale, culture is everything.
\nDefine your values early:
\nDocument your engineering principles:
\nRefer back to these constantly.
\nMake a decision framework public:
\nMost decisions are Type 2. Go fast.
\nMake career progression explicit:
\nClear frameworks reduce ambiguity.
\nDocument your technical strategy:
\nThis prevents 50 different approaches across 50 engineers.
\nYour job changes from \"build great stuff\" to:
\nScaling is hard. The skills that made you successful at small scale often work against you at large scale.
\nInvest in:
\nBuild an organization where good decisions happen at all levels, not just at the top.
\nThat's how you scale to 100+ engineers while maintaining quality and velocity.
", "url": "https://surajsrivastav.com/blog/engineering-leadership-at-scale/", "title": "Engineering Leadership at Scale", "summary": "Scaling an engineering organization is different from building a small team. The skills that made you successful as an engineer or small team leader don't automatically work at scale.", "date_modified": "2026-04-25T00:00:00.000Z", "author": { "name": "Suraj Srivastav", "url": "https://github.com/surajsrivastav" }, "tags": [ "Leadership" ] } ] }