Anti-dilution Provision

An anti-dilution provision is investor protection against down rounds: it adjusts the conversion price of preferred shares so existing investors are not diluted as much when new shares are issued at a lower price. Weighted average, broad-based anti-dilution is standard and most founder-friendly; full ratchet is punitive and uncommon outside distressed deals.

Investor protection against down rounds, adjusts the conversion price of preferred shares so existing investors aren't diluted as much when new shares are issued at a lower price. 'Weighted average' (broad-based) anti-dilution is standard and most founder-friendly. 'Full ratchet' anti-dilution is investor-friendly but punitive in a down round and is uncommon outside distressed deals.

The Data Nerd. "Anti-dilution Provision." VC Deal Flow Signal Glossary, https://signals.gitdealflow.com/glossary#anti-dilution-provision.


Monorepo

A monorepo is a single Git repository containing multiple distinct projects, applications, or libraries. It enables atomic cross-package changes, shared tooling, and simplified dependency management. Modern tooling like Nx, Turborepo, and Bazel addresses the build and test scalability problem. Companies like Google, Meta, and Linear run monorepos.

A single Git repository containing multiple distinct projects, applications, or libraries. Monorepos enable atomic cross-package changes, shared tooling, and simplified dependency management. Modern monorepo tooling (Nx, Turborepo, Lerna, Bazel) addresses the build and test scalability problem. Companies like Google, Meta, Microsoft, Vercel, and Linear run monorepos; engineering-signal pattern: monorepo orgs typically show flatter, more uniform commit distribution.

The Data Nerd. "Monorepo." VC Deal Flow Signal Glossary, https://signals.gitdealflow.com/glossary#monorepo.


Edge Function

An edge function is code executed at globally-distributed compute nodes physically close to end users, typically running in V8 isolates or Wasm sandboxes. Cloudflare Workers, Vercel Edge Functions, and Deno Deploy are the headline platforms. Edge functions trade execution-environment constraints for sub-100ms cold-start times and global low-latency reach.

Code executed at globally-distributed compute nodes physically close to end users, typically running in V8 isolates, Wasm sandboxes, or lightweight VMs. Cloudflare Workers, Vercel Edge Functions, Fly.io machines, and Deno Deploy are the headline platforms. Edge functions trade execution environment constraints for sub-100ms cold-start times and global low-latency reach. Increasingly used as the AI-inference gateway layer.

The Data Nerd. "Edge Function." VC Deal Flow Signal Glossary, https://signals.gitdealflow.com/glossary#edge-function.


Continuous Deployment

Continuous deployment is the software-delivery practice where every code change merged to main is automatically deployed to production, often within minutes. It is distinct from continuous integration, which only tests, and continuous delivery, which prepares releases. CD enables small batch sizes, rapid feedback, and lower per-change risk.

Software-delivery practice where every code change merged to main is automatically deployed to production, often within minutes. Distinct from continuous integration (which only tests) and continuous delivery (which prepares releases). CD enables small batch sizes, rapid feedback, and lower per-change risk. Companies that ship via CD include Stripe, GitHub, Vercel, Cloudflare, and most modern AI-infra orgs. Engineering-signal correlation: high-CD orgs show distinctive deploy-frequency-spike patterns in their public commit panel.

The Data Nerd. "Continuous Deployment." VC Deal Flow Signal Glossary, https://signals.gitdealflow.com/glossary#continuous-deployment.


Feature Flag

A feature flag is a runtime toggle controlling whether a feature is exposed to a given user, request, or environment, independently from deployment. Feature flags decouple deploy from release, enable A/B testing, support gradual rollout, and provide kill switches for production incidents. PostHog, LaunchDarkly, and Statsig are the headline platforms.

Runtime toggle controlling whether a feature is exposed to a given user, request, or environment, independently from deployment. Feature flags decouple deploy from release, enable A/B testing, support gradual rollout, and provide kill switches for production incidents. PostHog, LaunchDarkly, Statsig, and ConfigCat are the headline platforms. Standard practice at most modern product orgs.

The Data Nerd. "Feature Flag." VC Deal Flow Signal Glossary, https://signals.gitdealflow.com/glossary#feature-flag.


Distributed Tracing

Distributed tracing is the observability technique that follows a single request as it crosses multiple services, recording the timing and metadata of each hop. The standard format is OpenTelemetry; tools include Datadog APM, Honeycomb, and Grafana Tempo. It is the only effective debugging path in microservices architectures.

Observability technique that follows a single request as it crosses multiple services, recording the timing and metadata of each hop. Standard format: OpenTelemetry (OTel). Tools: Datadog APM, Honeycomb, Grafana Tempo, Lightstep (now ServiceNow), Sentry Performance. Distributed tracing is the only effective debugging path in microservices architectures; without it, root-cause analysis devolves into log-grepping across dozens of services.

The Data Nerd. "Distributed Tracing." VC Deal Flow Signal Glossary, https://signals.gitdealflow.com/glossary#distributed-tracing.


ReAct (Reasoning + Acting)

ReAct, Reasoning plus Acting, is a prompting framework for LLM agents, introduced by Yao et al. in 2022, that interleaves reasoning traces with action calls: the agent generates a thought, takes a tool action, observes the result, then continues. ReAct became the dominant agentic pattern in 2023-2024 before being partially superseded by trained tool use.

Prompting framework for LLM agents introduced by Yao et al. (2022) that interleaves reasoning traces with action calls. Each step: the agent generates a thought, takes an action (tool call), observes the result, then continues. ReAct became the dominant agentic pattern in 2023-2024 before being partially superseded by trained tool-use in modern frontier models. LangChain, CrewAI, and many early agent frameworks built around ReAct as the default loop.

The Data Nerd. "ReAct (Reasoning + Acting)." VC Deal Flow Signal Glossary, https://signals.gitdealflow.com/glossary#react-agent.


Agent Memory

Agent memory is persistent state an LLM agent maintains across turns, sessions, or interactions. Three common types: short-term, the current context window; episodic, recent conversation history retrieved via RAG; and long-term, facts and preferences stored in a separate database. Letta, formerly MemGPT, is the canonical reference implementation.

Persistent state an LLM agent maintains across turns, sessions, or interactions. Three common memory types: (1) short-term, the current context window; (2) episodic, recent conversation history retrieved via RAG; (3) long-term, facts and preferences stored in a separate database. Letta (formerly MemGPT) is the canonical reference implementation for persistent agent memory; modern frameworks (Mastra, CrewAI) ship memory primitives as a first-class concept.

The Data Nerd. "Agent Memory." VC Deal Flow Signal Glossary, https://signals.gitdealflow.com/glossary#agent-memory.


Multi-Agent Orchestration

Multi-agent orchestration is an architecture where multiple LLM agents with distinct roles collaborate on a task, coordinated by a meta-agent or an explicit workflow. CrewAI and AutoGen pioneered the pattern; LangGraph and Mastra extended it with state-machine-style coordination. Key design tradeoffs include role specialization, message-passing semantics, and failure handling.

Architecture where multiple LLM agents with distinct roles collaborate on a task, coordinated by a meta-agent or explicit workflow. CrewAI and AutoGen pioneered the multi-agent pattern; LangGraph and Mastra extended it with state-machine-style coordination. Critical design tradeoffs: agent role specialization, message-passing semantics, failure handling, and the orchestration overhead vs single-agent baselines.

The Data Nerd. "Multi-Agent Orchestration." VC Deal Flow Signal Glossary, https://signals.gitdealflow.com/glossary#multi-agent-orchestration.


Agent Evaluation

Agent evaluation is the discipline of measuring LLM agent capability, reliability, and safety across well-defined benchmarks. It is distinct from LLM evals because agent evals require multi-step trajectory measurement. Common benchmarks include SWE-bench for software engineering, tau-bench for tool use, WebArena for browser navigation, and AgentBench for general capability.

The discipline of measuring LLM agent capability, reliability, and safety across well-defined benchmarks. Distinct from LLM evals (which measure single-call performance) because agent evals require multi-step trajectory measurement. Common benchmarks: SWE-bench (software engineering), τ-bench (tool use), WebArena (browser navigation), AgentBench (general capability). Vendors: Braintrust, Galileo, Inspect AI, LangSmith.

The Data Nerd. "Agent Evaluation." VC Deal Flow Signal Glossary, https://signals.gitdealflow.com/glossary#agent-evaluation.


Agent Guardrails

Agent guardrails are runtime constraints that limit agent behavior: output filtering, tool-call validation, spend limits, time budgets, and harmful-action detection. Implementations include NVIDIA NeMo Guardrails and Guardrails AI. Distinct from training-time alignment like RLHF, guardrails operate at inference time and can be customized per deployment.

Runtime constraints that limit agent behavior, output filtering, tool-call validation, spend limits, time budgets, and harmful-action detection. Implementations include NVIDIA NeMo Guardrails, Guardrails AI, and platform-specific safety APIs (Anthropic, OpenAI). Distinct from training-time alignment (RLHF, Constitutional AI), guardrails operate at inference time and can be customized per deployment.

The Data Nerd. "Agent Guardrails." VC Deal Flow Signal Glossary, https://signals.gitdealflow.com/glossary#agent-guardrails.


Planning Agent

A planning agent is an LLM agent designed to decompose a high-level goal into a sequence of sub-tasks before executing. It typically uses chain-of-thought reasoning to construct the plan and a separate execution loop to carry it out. Examples include Devin, Claude Code, and AutoGen Planner; modern reasoning models can serve as planning agents natively.

An LLM agent designed to decompose a high-level goal into a sequence of sub-tasks before executing. Planning agents typically use Chain-of-Thought reasoning to construct the plan and a separate execution loop to carry it out. Examples: Devin (Cognition), Claude Code (Anthropic), AutoGen Planner. Modern reasoning models (OpenAI o1/o3, Claude with extended thinking) can serve as planning agents natively.

The Data Nerd. "Planning Agent." VC Deal Flow Signal Glossary, https://signals.gitdealflow.com/glossary#planning-agent.


Long-running Agent

A long-running agent is an LLM agent designed to execute a task over hours, days, or longer, across multiple sessions, with persistent state and resilience to interruption. Examples include Claude with computer use and autonomous coding agents like Devin. They require durable workflow infrastructure such as Temporal or Inngest plus explicit checkpoint-resume primitives.

An LLM agent designed to execute a task over hours, days, or longer, across multiple sessions, with persistent state, and resilient to interruption. Examples: Anthropic's Claude with computer use, OpenAI's o1-Pro mode for extended reasoning, autonomous coding agents (Devin). Long-running agents require durable workflow infrastructure (Temporal, Inngest) and explicit checkpoint-resume primitives.

The Data Nerd. "Long-running Agent." VC Deal Flow Signal Glossary, https://signals.gitdealflow.com/glossary#long-running-agent.


Tool Bench

A tool bench is a benchmark suite that measures LLM agent capability at calling, chaining, and reasoning over real-world APIs. The original ToolBench from 2023 covered 16K+ APIs across 49 categories; modern variants like the Berkeley Function Calling Leaderboard and tau-bench refine the measurement. Frontier labs publish tool-use scores as proxies for agent capability.

Benchmark suite that measures LLM agent capability at calling, chaining, and reasoning over real-world APIs. The original ToolBench (2023) covered 16K+ APIs across 49 categories. Modern variants (Berkeley Function Calling Leaderboard, τ-bench) refine the measurement. Frontier labs publish tool-use scores on these benchmarks as proxies for agent capability.

The Data Nerd. "Tool Bench." VC Deal Flow Signal Glossary, https://signals.gitdealflow.com/glossary#tool-bench.


LPA (Limited Partner Agreement)

An LPA, Limited Partner Agreement, is the legal document governing the relationship between a venture fund and its investors. It specifies fund size, investment period, fund life, typically 10 years, management fee, carried interest, GP commit, distribution waterfall, and key-person provisions. It is the foundational document of fund formation.

The legal document governing the relationship between a venture capital fund (the General Partner) and its investors (Limited Partners). Specifies fund size, investment period, fund life (typically 10 years), management fee, carried interest, GP commit, distribution waterfall, and key-person provisions. The LPA is the foundational document of fund formation; LP-side legal counsel scrutiny on LPA terms is one of the longest stages of new-fund formation.

The Data Nerd. "LPA (Limited Partner Agreement)." VC Deal Flow Signal Glossary, https://signals.gitdealflow.com/glossary#lpa.


GP Commit

GP commit is the capital that the General Partner commits to its own fund, expressed as a percentage of fund size. Industry standard ranges from 1% for institutional funds to 5%+ for emerging managers, where LPs require higher skin-in-the-game. GP commit is one of the strongest fund-quality signals LPs evaluate during diligence.

The capital that the General Partner (the VC firm) commits to its own fund, expressed as a percentage of fund size. Industry standard ranges from 1% (institutional funds) to 5%+ (emerging-manager funds where LPs require higher skin-in-the-game). GP commit is one of the strongest fund-quality signals LPs evaluate; a low or financed GP commit is often a red flag during LP diligence.

The Data Nerd. "GP Commit." VC Deal Flow Signal Glossary, https://signals.gitdealflow.com/glossary#gp-commit.


Carried Interest (Carry)

Carried interest, or carry, is the General Partner's share of fund profits above the LPs' return of capital, typically after a preferred return hurdle of 6-8%. Industry standard is 20% carry on the upside, though high-performing funds can negotiate 25-30%. Carry is the primary economic incentive aligning GPs with LPs.

The General Partner's share of fund profits above the LPs' return of capital (and typically a preferred return hurdle of 6-8%). Industry standard is 20% carry on the upside, though high-performing funds can negotiate 25-30%. Carry is the primary economic incentive aligning GPs with LPs and is taxed favorably as long-term capital gains in most jurisdictions.

The Data Nerd. "Carried Interest (Carry)." VC Deal Flow Signal Glossary, https://signals.gitdealflow.com/glossary#carried-interest.


Management Fee

A management fee is the annual fee a venture fund charges its LPs to cover operating costs, typically 2% of committed capital during the investment period and 2% of invested capital during the harvest period. Emerging-manager funds occasionally negotiate 2.5-3%; large established funds sometimes step down to 1.5%.

The annual fee a venture fund charges its LPs to cover operating costs, typically 2% of committed capital during the investment period and 2% of invested capital during the harvest period. Tier-1 emerging-manager funds occasionally negotiate 2.5-3% to support more GP infrastructure; large established funds sometimes step down to 1.5%. Management fees are deducted from LP commitments and reduce the net IRR returns LPs ultimately realize.

The Data Nerd. "Management Fee." VC Deal Flow Signal Glossary, https://signals.gitdealflow.com/glossary#management-fee.


Vintage Year

A vintage year is the year a fund made its first investment, used by LPs to benchmark fund performance against peers raised in the same market environment. Vintage matters because returns are heavily macro-correlated: a 2020 vintage fund has different return-expectation context than a 2008 vintage. PitchBook and Cambridge Associates publish vintage-year benchmarks.

The year a fund made its first investment, used by LPs to benchmark fund performance against peers raised in the same market environment. Vintage matters because returns are heavily macro-correlated; a 2020 vintage fund has different return-expectation context than a 2008 vintage. PitchBook, Cambridge Associates, and Preqin publish vintage-year benchmarks that LPs use during fund evaluation.

The Data Nerd. "Vintage Year." VC Deal Flow Signal Glossary, https://signals.gitdealflow.com/glossary#vintage-year.


Capital Call

A capital call is a formal request from a venture fund's General Partner to its Limited Partners to wire committed but not-yet-funded capital to the fund. LPs typically receive capital calls 10-15 days before the deadline with the dollar amount, allocation purpose, and wiring instructions. Defaults are extremely rare but do happen during economic distress.

A formal request from a venture fund's General Partner to its Limited Partners to wire committed but not-yet-funded capital to the fund. LPs typically receive capital calls 10-15 days before the deadline with the dollar amount, allocation purpose, and wiring instructions. Capital call defaults are extremely rare (default would forfeit the LP's stake) but do happen during economic distress.

The Data Nerd. "Capital Call." VC Deal Flow Signal Glossary, https://signals.gitdealflow.com/glossary#capital-call.


Deployment Frequency (DORA)

Deployment frequency is one of the four DORA metrics: how often code is deployed to production. Elite teams deploy multiple times per day; high performers once per day to once per week; medium performers once per month to once per six months. High deployment frequency correlates with lower batch sizes and faster feedback.

One of the four DORA metrics measuring software-delivery performance. Counts how often code is deployed to production: elite teams deploy multiple times per day; high-performing teams deploy once per day to once per week; medium-performing teams deploy once per month to once per six months; low-performing teams deploy less than once per six months. High deployment frequency correlates with lower batch sizes, faster feedback, and higher organizational reliability.

The Data Nerd. "Deployment Frequency (DORA)." VC Deal Flow Signal Glossary, https://signals.gitdealflow.com/glossary#deployment-frequency.


Lead Time for Changes (DORA)

Lead time for changes is one of the four DORA metrics: the time between a code commit and that code running successfully in production. Elite teams achieve less than 1 day; high performers 1 day to 1 week; medium 1 week to 1 month; low 1 month to 6 months.

One of the four DORA metrics, the time between a code commit and that code running successfully in production. Elite teams: less than 1 day. High performers: 1 day to 1 week. Medium: 1 week to 1 month. Low: 1 month to 6 months. Short lead time enables rapid iteration on customer feedback and emergency security patches. Long lead time often correlates with batch-up-and-release-monthly culture and brittle deployment pipelines.

The Data Nerd. "Lead Time for Changes (DORA)." VC Deal Flow Signal Glossary, https://signals.gitdealflow.com/glossary#lead-time-for-changes.


Change Failure Rate (DORA)

Change failure rate is one of the four DORA metrics: the percentage of changes to production that result in degraded service, requiring hotfix, rollback, or remediation. Elite and high-performing teams sit at 0-15%; medium and low performers at 16-30%. DORA research shows high performers have both high deployment frequency and low failure rate.

One of the four DORA metrics, the percentage of changes to production that result in degraded service, requiring hotfix, rollback, or remediation. Elite and high-performing teams: 0-15%. Medium and low performers: 16-30%. Despite intuition that high deployment frequency causes more failures, DORA research shows the opposite, high performers have BOTH high deployment frequency AND low change failure rate, because small batches reduce per-change risk.

The Data Nerd. "Change Failure Rate (DORA)." VC Deal Flow Signal Glossary, https://signals.gitdealflow.com/glossary#change-failure-rate.


Red-Teaming (AI)

AI red-teaming is adversarial testing of LLMs by humans or other AI systems attempting to elicit harmful, unsafe, or undesired behaviors. The goal is to surface jailbreaks, alignment failures, and security vulnerabilities before public release. Anthropic, OpenAI, Google DeepMind, and Meta all run internal red teams; the practice has matured into a distinct discipline.

Adversarial testing of LLMs by humans (or other AI systems) attempting to elicit harmful, unsafe, or undesired behaviors. Goals: surface jailbreaks, alignment failures, security vulnerabilities, and miscalibrated capabilities before public release. Anthropic, OpenAI, Google DeepMind, and Meta all run internal red-teams; the practice has matured into a distinct discipline with publishing venues (Anthropic Red-Teaming Network, OpenAI's evals team).

The Data Nerd. "Red-Teaming (AI)." VC Deal Flow Signal Glossary, https://signals.gitdealflow.com/glossary#red-teaming-ai.


Jailbreak

A jailbreak is a prompt or sequence of prompts designed to bypass an LLM's safety training and elicit prohibited behaviors. Common patterns include role-playing as an uncensored AI, fictional framing, multi-turn manipulation, and adversarial token sequences. Modern frontier models substantially harden against jailbreaks, but the cat-and-mouse continues.

A prompt or sequence of prompts designed to bypass an LLM's safety training and elicit prohibited behaviors. Common patterns: role-playing as an uncensored AI, fictional framing, multi-turn manipulation, and adversarial token sequences. Modern frontier models substantially harden against jailbreaks via Constitutional AI training and runtime input filtering, but the cat-and-mouse continues.

The Data Nerd. "Jailbreak." VC Deal Flow Signal Glossary, https://signals.gitdealflow.com/glossary#jailbreak.


Eval (LLM Benchmark)

An LLM eval is a structured benchmark measuring model capability on a specific task. Common evals include MMLU for broad academic knowledge, HumanEval for Python code generation, GSM8K for math word problems, GPQA for graduate-level science, and SWE-bench for software engineering. Evals drive reward modeling and serve as the screening test for model releases.

A structured benchmark measuring LLM capability on a specific task. Common evals: MMLU (broad academic knowledge), HumanEval (Python code generation), GSM8K (math word problems), MATH (advanced math), GPQA (graduate-level science), SWE-bench (software engineering trajectories). Eval-driven development is the foundation of modern LLM training: evals provide the loss signal for reward modeling, the screening test for model releases, and the comparative basis for cross-lab model comparison.

The Data Nerd. "Eval (LLM Benchmark)." VC Deal Flow Signal Glossary, https://signals.gitdealflow.com/glossary#eval-llm.