JIGAR JOSHI

Every PR workflow is burning tokens. GitHub reduced token waste by: → Measuring per run → Removing verbose context → Adding constraint rules Lower cost. Same quality. Read: github.blog/ai-and-ml/gith… #AI #AgenticAI #LLM #GitHubCopilot

Your MCP works in dev. Fails in production. Missing layers: Auth → Limit → Sanitize → Log Dev = fast Prod = safe #AI #AIAgents #SystemDesign #APISecurity

Your AI agent gets dumber over time. Not a bug. Context fills up. Focus drifts. Quality drops. Three fixes: - Long session? Summarize. - Output drifting? Prune. -Quality crashed? Restart. Manage the context. Or lose the agent. #AIAgents #ContextManagement #PromptEngineering

Your AI agent failed. Logs show the output. Not why the decision went wrong. Trace the decision. Fix the prompt. Not the output. Debugging agents is a prompting skill. #AIAgents #PromptEngineering #AgenticAI #AITools #LLM

Most AI agents fail mid-task. Not because of intelligence. Because of missing planning. Plan → Validate → Execute Simple rule. Massive difference. #AI #AIAgents #Automation

Your AI agent needs memory. But not all memory is the same. Vector DB → fast similarity Graph RAG → deep reasoning Different problems. Different tools. Smart systems use BOTH. #AI #RAG #GraphRAG #AIAgents

Two agents. Same file. One writes. One reads. That's a race condition. Fix it with: • Shared State • Write Locks • Conflict Resolution Structure first. Scale second. #AgenticAI #MultiAgent #AIArchitecture

Most AI agents are demos pretending to be products. What actually makes them production-read • Validate every input • Execute on real APIs & DBs • Handle errors - structured, never raw • Retry on 429 + 503 Production agents don't guess. They follow systems. #AgenticAI #LLM

Open source is free. But it’s not free of responsibility. Ownership exists Contributions are trust Legal isn’t always ethical Community drives everything Spoke about this at OSW—important conversations ahead. #OpenSource #EthicsInTech

Most developers never create .github/copilot-instructions.md. Add your project type, agent patterns and banned antipatterns. Copilot goes from generic Python → your architecture patterns. Takes 15 minutes. Big upgrade. #GitHubCopilot #AIEngineering #CodingTips #DevTools #AI

Agent quality drops at turn 12. Not the model. Context window past 70 percent. Log input_tokens at every loop step. Trigger compression at 70 percent. Summarise turn history into a state snapshot. Keep system prompt and tool defs. Compress everything else. #AI #Automation

CLAUDE.md is read at every Claude Code session start. 4 sections that make it use your knowledge, not generic knowledge: Project context Coding standards Agent architecture Write once. Works every session. #ClaudeCode #AgenticAI #CLAUDEmd

System prompt drift fix — 4 sections that never change: 1.Role 2.Task 3.Context 4.Output Format Only your variables change across turns. That separation keeps agent behaviour consistent from turn 1 to turn 20. #PromptEngineering #AgenticAI #SystemPrompt #LLMDev

🗂️ .cursorrules — 20 mins that saves weeks of drift. One file. Cursor reads it on every prompt. → Directory structure → Base classes → Banned patterns Write once, enforce always. ⚡ #CursorAI #AgenticAI #CursorRules #DevTools

Your agentic AI loop has no exit condition. That is why it loops forever on failure. Fix: set max_iterations at architecture level, not in the prompt. Separate tool state from reasoning state. Log every observe step. Three changes before you write anything else. #AgenticAI

So much business data already exists. It’s just unstructured and easy to ignore. We’re building @ScanDoc to change that one document at a time.

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