Build an AI Agent (From Scratch) vs SavirOS

Teaches patterns for binding external tools (APIs, functions, services) to AI agents through structured schemas and invocation mechanisms. Covers tool discovery, parameter binding, error handling, and result parsing to enable agents to autonomously select and execute appropriate tools during task execution.

Unique: Provides systematic patterns for designing tool registries and invocation mechanisms that work across multiple LLM providers (OpenAI, Anthropic, etc.) rather than single-provider implementations, with emphasis on graceful degradation and error recovery

vs alternatives: More comprehensive than provider-specific tool-calling docs because it abstracts patterns across LLM ecosystems and covers multi-agent tool coordination scenarios

Describes strategies for maintaining agent state across multiple reasoning steps, including short-term working memory, long-term knowledge storage, and context window optimization. Covers memory architectures like sliding windows, summarization, vector embeddings for retrieval, and hybrid approaches to balance context relevance with token constraints.

Unique: Systematically covers memory trade-offs across agent lifecycle (working memory vs. long-term storage, retrieval latency vs. relevance) with patterns for hybrid approaches rather than single-strategy recommendations

vs alternatives: More holistic than individual RAG or context-management tutorials because it positions memory as a core architectural decision affecting agent autonomy, cost, and reasoning quality

Teaches methodologies for breaking complex tasks into sub-goals and reasoning steps, including chain-of-thought prompting, tree-of-thought search, and hierarchical planning. Covers how agents can decompose ambiguous user requests into concrete action sequences, evaluate alternative plans, and adapt when execution fails.

Unique: Covers planning as a spectrum from simple linear decomposition to tree-search and hierarchical approaches, with explicit guidance on when to use each pattern based on task complexity and computational budget

vs alternatives: More comprehensive than single-pattern tutorials (e.g., just chain-of-thought) because it addresses planning as a core architectural choice affecting agent autonomy and reasoning quality

Describes patterns for orchestrating multiple specialized agents working toward shared goals, including message passing, role assignment, consensus mechanisms, and conflict resolution. Covers how agents can delegate tasks, share context, and coordinate execution without central control.

Unique: Treats multi-agent coordination as a first-class architectural pattern with explicit guidance on communication protocols, role hierarchies, and conflict resolution rather than treating it as an extension of single-agent design

vs alternatives: More systematic than ad-hoc multi-agent examples because it covers coordination patterns (hierarchical, peer-to-peer, publish-subscribe) and their trade-offs

Teaches the core agent loop architecture: perception (observing state), reasoning (deciding actions), and action (executing decisions). Covers how to implement feedback loops, handle execution results, and determine when agents should stop or escalate to humans. Includes patterns for balancing autonomy with safety constraints.

Unique: Frames the agent loop as a control system with explicit feedback mechanisms and safety constraints rather than a simple request-response pattern, emphasizing the role of observation and adaptation

vs alternatives: More foundational than tool-calling or planning tutorials because it addresses the core loop that makes agents autonomous and provides patterns for safe, bounded autonomy

Describes methodologies for measuring agent performance, including task success metrics, reasoning quality assessment, and cost-efficiency analysis. Covers how to design test suites for agent behavior, handle non-deterministic outputs, and benchmark against baselines. Includes patterns for continuous evaluation and improvement.

Unique: Addresses evaluation as a core architectural concern rather than an afterthought, with patterns for handling non-deterministic outputs and continuous improvement cycles

vs alternatives: More comprehensive than generic LLM evaluation because it addresses agent-specific challenges like multi-step reasoning quality and cost-per-task optimization

Teaches patterns for detecting agent failures (execution errors, invalid outputs, timeout), implementing recovery strategies (retry with backoff, alternative tool selection, task decomposition), and graceful degradation. Covers how to distinguish recoverable errors from fundamental failures and when to escalate to humans.

Unique: Treats error recovery as a core agent capability with explicit patterns for classification, retry strategies, and escalation rather than generic exception handling

vs alternatives: More agent-specific than generic error handling because it addresses multi-step reasoning failures and distinguishes between tool failures, reasoning errors, and LLM output issues

Describes techniques for crafting effective prompts that guide agent behavior, including role definition, task specification, constraint encoding, and output formatting. Covers how to structure instructions for multi-step reasoning, tool use, and error recovery. Includes patterns for prompt versioning and A/B testing.

Unique: Treats prompt engineering as a systematic discipline with patterns for role definition, constraint encoding, and output formatting rather than ad-hoc trial-and-error

vs alternatives: More agent-focused than generic prompt engineering guides because it addresses multi-step reasoning, tool use, and error recovery in prompts

SavirOS is an AI-powered Relationship Operating System that enhances meeting preparation by auto-generating intelligence briefs, tracking promises, and compiling relationship memory, ensuring users are always prepared and informed for their meetings.

Unique: SavirOS uniquely compounds relationship intelligence across all interactions, making it smarter with each meeting unlike competitors that treat meetings in isolation.

vs alternatives: SavirOS offers a more integrated and intelligent approach to meeting preparation compared to traditional tools that focus solely on transcription or note-taking.

SavirAI is a triage-RAG agent that answers questions about relationships, schedules actions, drafts emails, generates documents, and manages contacts — all through natural conversation. 84 tools across 7 agents: platform, calendar, relationship, pre-meeting, post-meeting, communication, creation. Autonomy policy gates sensitive actions (email sending, rescheduling) behind user confirmation.

Seven AI-powered generators for meeting-related communications: icebreaker conversation starters, meeting agenda generator, follow-up email drafts, email subject line optimizer, meeting decline message writer, introduction email generator, and out-of-office reply creator. All free, no signup required.

Automatically enriches contacts with LinkedIn profile data (Proxycurl), company intelligence (Hunter.io), recent news (NewsData.io), and web search (Tavily). Creates comprehensive contact profiles with career history, company details, mutual connections, and recent activity.

Four utility tools: QR code generator (URL, WiFi, vCard, text — PNG/SVG export), browser-based image compressor (JPEG/PNG/WebP, no upload), JSON formatter/validator with tree view, and file sharing (up to 50MB, shareable links). All free, no signup, privacy-first.

Four free lookup tools: reverse caller ID (global, spam detection, confidence scoring), professional email finder (Hunter.io verification), person lookup (career history, talking points via Proxycurl/Tavily), and company lookup (industry, funding, team size, news, social links).

Five meeting utilities: real-time meeting timer with agenda tracking, meeting link decoder (extracts ID/passcode from Zoom/Teams/Meet URLs), instant meeting link generator, WhatsApp link builder with prefilled messages, and downloadable .ics calendar event creator.

Auto-detects ended meetings (every 3 minutes). Processes transcripts from Recall.ai, Fireflies.ai, or user-pasted notes. Extracts structured summary, key points, decisions (with rationale and decision maker), and commitments. Builds episodic memory records. Extracts individual facts and consolidates into per-contact intelligence profiles.