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# Practices for Embedding AI Agents in Software # Eval Harness 🎯 The Hook Changed a prompt and quality silently dropped. The provider updated the model and accuracy drifted. You found out from user complaints. An eval harness catches these before deployment. 🔥 The Problem AI agents are probabilistic -- deterministic unit tests don't apply. Hallucinations can't be caught by spot-checking individual outputs. LLM drift (quality shifts from provider-side model updates) happens even when your team changes nothing. Without an eval harness, quality degradation surfaces only as production incidents and user complaints. 💡 The Pattern Build an evaluation pipeline combining golden datasets (input + expected output pairs) with automated eval functions (exact match, LLM-as-Judge, custom metrics). Run regression detection in CI on every prompt, model, or code change. Schedule periodic runs (daily to weekly) even without changes to catch LLM drift. Evaluation operates at three layers: offline (pre-deploy batch), online (production sampling), and shadow (A/B comparison). Regression gate thresholds are category-specific, with zero-tolerance for safety category degradation. ✅ When to Use Use when: - The agent handles production traffic with ongoing prompt, model, or code changes - Quantitative quality evidence is required (SLA compliance, regulatory reporting) - Domain experts can create and maintain golden datasets Don't use when: - Prototype stage where prompts change drastically daily - Output is creative generation (fiction, poetry) where "correct" is hard to define - Downstream deterministic validation already auto-corrects errors ⚠️ Pitfalls - Golden data rots. When product specs change, outdated expected outputs flag correct results as regressions. Assign owners and last-updated dates, and schedule regular reviews - LLM-as-Judge is also probabilistic. Even at temperature 0, scores can vary. For critical cases, use majority vote across 3 runs - Define when baseline scores are updated (e.g., after successful deploy). Without explicit update triggers, degradation becomes invisible 🔧 Implementation Approach - Structure golden datasets with category, eval function name, and expected keywords per case, enabling category-level aggregation and regression detection - Operate the eval pipeline at three layers: offline (pre-deploy batch in CI), online (production sampling), and shadow (A/B comparison of old vs. new) - Set regression gates as relative comparisons against baseline scores -- zero tolerance for safety categories, roughly 2% degradation threshold for general categories - In shadow mode, dry-run any side-effecting tool calls; in canary deploys, use sticky sessions to keep users on the same version - Assign owners and last-updated dates to golden data to prevent rot from spec changes, with scheduled periodic reviews #AIAgents# #SoftwareArchitecture#

# Decision Points in AI Agent Development 🎯 The Hook Single LLM provider or multi-provider? "We should use multiple providers for resilience" sounds prudent, but have you priced in the real cost? Maintaining abstraction layers, tuning prompts per provider, running parallel quality evaluations. The overhead of multi-provider is routinely underestimated. On the other hand, are you comfortable betting everything on one vendor? 📋 Overview This is about whether your AI agent should use a single LLM provider (OpenAI, Anthropic, Google, Azure OpenAI, etc.) or multiple providers in parallel. "Provider" here means the API vendor, not different models within the same vendor. Each provider differs in API specification (tool calling format, streaming protocol, error codes), pricing, rate limits, and availability SLA. Moving to multi-provider ripples through infrastructure, prompts, and evaluation -- operational cost increases non-linearly. 🔍 Decision Points The key axes are provider trust and system availability requirements. 📌 SLA requirement is 99.9%+ and the provider's track record falls short → Consider multi-provider. LLM provider outages and rate limits tend to be more frequent and less predictable than traditional SaaS. 📌 Heavy dependency on provider-specific features (prompt caching, batch API, fine-tuning) → Stay single-provider. An abstraction layer reduces you to the lowest common denominator, losing provider-specific optimizations. 📌 Small team → Stay single-provider. If you lack the resources to maintain parallel prompt tuning and quality evaluation across providers, quality management on the secondary provider will suffer. 📌 Regulatory or data sovereignty requirements → Multi-provider. When certain requests must be routed to specific regions or providers, there is no alternative. 💡 Key Details 🔹 Single provider's greatest asset is depth of optimization. You can focus prompt tuning on one model family and leverage prefix caching and context caching for major cost and latency reductions. Output quality, style, and structured output precision remain stable. 🔹 Multi-provider's greatest asset is fault tolerance. When Provider A goes down, automatic failover to Provider B maintains availability. Task-based provider selection also enables cost optimization. The "always ready to switch" posture doubles as negotiation leverage. 🔹 The hidden costs of multi-provider: building and continuously maintaining adapters that absorb API differences (tool calling formats, streaming protocols, error codes) across providers. The same prompt performs differently across providers, requiring per-provider prompt tuning and evaluation. Pre-validating quality degradation during failover scenarios. ⚖️ Trade-offs Single provider gains depth of optimization and operational simplicity at the cost of vendor lock-in and availability risk. Multi-provider gains fault tolerance and strategic flexibility at the cost of abstraction layer maintenance and quality management complexity. 🏆 The proven approach: start with a single provider behind an abstraction layer. Use something like LiteLLM or an AI Gateway from day one, even with just one provider. When availability or cost issues surface, adding a second provider becomes a manageable migration rather than a rewrite. Direct integration without an abstraction layer makes future migration prohibitively expensive. 🛠️ Use Cases 🚀 Startup MVP → Single provider with abstraction layer. Prioritize development speed, but keep the door open for multi-provider later. 🏥 High-SLA systems (healthcare, finance) → Multi-provider. When downtime is unacceptable, pre-validate your failover target and combine with circuit breaker patterns for automatic switchover. 🌍 Global deployment with data sovereignty → Multi-provider. Route EU requests to EU-based providers, Japan requests to domestic regions. 📊 High-traffic systems → Multi-provider. Distribute traffic across providers for rate limit relief and cost optimization. #AIAgents# #SoftwareArchitecture#

# Practices for Embedding AI Agents in Software # Short DB Transaction, Long Agent Session 🎯 The Hook Holding a database transaction open while the LLM thinks for 30 seconds? Your connection pool will starve and the entire system grinds to a halt. Separate the long session from the short transaction. 🔥 The Problem Traditional web apps finish request processing in milliseconds, so request-scoped transactions work fine. AI agents are different -- LLM calls take seconds to minutes. Keeping a transaction open during inference causes connection pool exhaustion, deadlocks, lock contention, and transaction timeouts. With P99 latency often several times P50, designs that work "on average" fail at the tail. 💡 The Pattern Never hold a transaction or database connection during LLM inference. Read data in a short transaction, commit and release the connection, call the LLM, then write results in a new short transaction. Intermediate state between steps is persisted to an external state store (Redis, DynamoDB, or a session table). Use optimistic locking (version columns) to handle data races during inference -- never hold pessimistic locks across LLM calls. This reduces required connection pool size to roughly 10-20% of concurrent sessions. ✅ When to Use Use when: - The agent reads from and writes to a database around LLM calls - LLM call latency exceeds roughly 1 second (most LLM calls qualify) - Concurrent session count may reach half the connection pool size Don't use when: - Traditional CRUD processing with no LLM involvement - Operations that must complete atomically in a single transaction (e.g., bank transfers) ⚠️ Pitfalls - Between read and write, another process may update the same row. Always use optimistic locking (version column or updated_at check) - Size your connection pool for concurrent transaction steps, not concurrent sessions. This pattern dramatically reduces the required pool size - For sessions involving irreversible operations, choose a state store with disk persistence over purely in-memory stores 🔧 Implementation Approach - Follow the sequence: short read transaction, commit, release connection, LLM call, re-acquire connection, short write transaction -- never hold transactions or connections during inference - Include optimistic locking (version column or updated_at) in write transactions to handle data races during LLM inference via retry - Persist intermediate state between steps in an external state store (Redis, DynamoDB, or RDB session table) to enable recovery after crashes - Size connection pools for concurrent transaction steps, not concurrent sessions -- typically 10-20% of concurrent session count suffices - For sessions involving irreversible operations, select a state store with disk persistence to meet durability requirements #AIAgents# #SoftwareArchitecture#

# Practices for Embedding AI Agents in Software # Verified Structured Output 🎯 The Hook Feeding raw LLM output into downstream systems? Broken JSON, invalid values, missing fields -- things fail silently until they become production incidents. Two-layer verification stops this. 🔥 The Problem LLM output is inherently ambiguous natural language. When downstream code expects strict types and fields, parse failures, type errors, and invalid values silently corrupt processing. Even when the format is correct, business rule violations like future dates in the past or nonexistent product IDs slip through syntax checks undetected. 💡 The Pattern Enforce structure in two layers. Layer 1 (schema validation) uses response_format or function_calling to constrain output to a JSON Schema. Layer 2 (semantic validation) applies deterministic code to verify business rules -- date ranges, ID existence, permission checks. On failure, error details are fed back into context for self-correction retry (1-3 attempts). If retries exhaust the budget, fail-fast explicitly rather than passing bad data downstream. Temperature is set to 0.0-0.3 for high schema compliance. ✅ When to Use Use when: - LLM output is consumed by downstream code, APIs, or databases - A target schema (types, required fields, value ranges) can be defined - Invalid output causes real business impact in downstream processing Don't use when: - Output is free-form text (summaries, conversations) with no structural requirements - The output schema changes so frequently that maintenance cost exceeds benefit - Invalid output can simply be discarded with no consequence ⚠️ Pitfalls - Appending full error messages on each retry inflates token consumption. Pass only the latest error or a summary - Schemas with too many fields (roughly 15+) reduce LLM compliance rates. Consider splitting into multiple calls - response_format is provider-specific. Manage schema definitions in a portable format like Pydantic or JSON Schema 🔧 Implementation Approach - Layer 1 (schema validation) uses response_format or function_calling to enforce JSON Schema compliance, with Pydantic models for type and value range constraints - Layer 2 (semantic validation) applies deterministic code to verify domain constraints such as date ranges, ID existence, and permissions - On validation failure, feed error details back into context for self-correction retry, with a budget of 1-3 attempts before fail-fast - Set LLM temperature to 0.0-0.3 to maximize structured output compliance rates - Manage schema definitions in a provider-agnostic format (Pydantic, JSON Schema) to ensure portability across LLM providers #AIAgents# #SoftwareArchitecture#

# Decision Points in AI Agent Development # Prompt Control vs Code/Policy Control 🛡️ 🎯 The Hook When constraining agent behavior, you either "ask nicely" via prompt or "build a wall" in code. This distinction maps directly to probabilistic compliance versus deterministic enforcement. Writing "do not output personal information" in a prompt can be overridden by prompt injection or diluted by context expansion. A regex that detects and blocks PII patterns works reliably regardless of what the LLM decides. The real question is: which constraints do you elevate to code? 📋 Overview Prompt control writes constraints into the LLM's system prompt or instructions to guide behavior. Typical techniques include few-shot examples, negative instructions, and role setting. Code/policy control implements constraints as executable code (conditionals, regex, schema validation) or via policy engines (OPA/Rego, Cedar, etc.), forcibly controlling LLM inputs, outputs, and operations. 🔍 Decision Points Two variables drive this decision: **failure cost** and **accountability**. 📊 **By constraint type**: Enforce with code/policy: - 🔒 Boundary and legal constraints (PII output prohibition, amount limits, forbidden operations) - 🔒 Irreversible operation restrictions (deletion, transfers, publication) - 🔒 Output format enforcement (JSON schema, value range validation) Guide with prompts: - 💬 Response quality and style (tone, politeness, technical terminology usage) - 💬 Context-dependent judgment guidelines (what to prioritize under which circumstances) 💡 Key Details 🟢 **Prompt control excels in flexibility and expressiveness.** Written in natural language, it can instruct subtle nuances and context-dependent behavior. "Use formal language for legal teams, allow technical jargon for engineers" is naturally expressed in a prompt. Changes require only rewriting the prompt -- no deployment. The weakness is low reliability: constraints can break under prompt injection, context expansion, and model updates. Version control, testing, and auditing are also difficult. 🟡 **Code/policy control excels in certainty and auditability.** Constraints are version-controlled as code, verified by unit tests, and preserved as audit trails. "Why was this output blocked?" is uniquely identified by the policy version and rule ID. It is immune to prompt injection and model updates. With policy engines, rule changes can be reflected without deployment. The weakness is limited expressiveness -- qualitative constraints like "respond politely" cannot be expressed in code. ⚖️ Trade-offs | Dimension | Prompt Control | Code/Policy Control | |---|---|---| | Reliability | Probabilistic (can break) 🔴 | Deterministic 🟢 | | Expressiveness | High (nuance capable) 🟢 | Low (condition → judgment only) | | Auditability | Requires prompt history tracking | Version control + audit trails 🟢 | | Injection resistance | Vulnerable | Immune | | Change velocity | Prompt rewrite only | Policy update or deployment | | Development cost | Low | Infrastructure + skills needed | 🛠️ Use Cases 🔵 **Prompt control fits**: Response tone and style guidance, context-dependent judgment guidelines, cases where constraint violation impact is minor. 🔴 **Code/policy control fits**: PII and financial data output control, irreversible operation restrictions, regulated processes, strict output format validation. 📌 **Default strategy**: "Boundaries (constraints that must not break) go in code; guidance (desirable behavior) goes in prompts." When in doubt, ask yourself: "How much does it hurt if this constraint is violated?" If the pain is significant, enforce it in code. If minor, guide it via prompt. The most practical architecture is a two-layer structure: policy enforcement plus prompt guidance. Code/policy deterministically enforces boundaries while prompts guide LLM behavior within those boundaries. Even when prompt guidance fails, policy acts as the final safety net. #AIAgents# #SoftwareArchitecture#

# Practices for Embedding AI Agents in Software # Risk-based Human Approval 🎯 The Hook "Let the agent do everything" and "approve everything manually" are both wrong. The answer is dynamic risk-based routing: auto-execute, require approval, or forbid -- based on each operation's actual risk. 🔥 The Problem When agents can execute operations with real-world side effects, full automation risks irreversible damage, while blanket approval requirements kill throughput. Telling the LLM to "ask if it seems dangerous" fails because that judgment itself is probabilistic and unreliable. 💡 The Pattern Classify every operation by risk score (irreversibility x failure cost) into three tiers. Low-risk operations (reads, reversible writes) auto-execute. Medium-to-high risk (irreversible or costly) requires human approval. Extreme risk (irreversible + catastrophic) is forbidden outright. Classification uses a deterministic rule engine, never the LLM itself. Approval timeouts default to rejection (safe side). An optional Autonomy Ladder dynamically adjusts thresholds based on the agent's track record over time. ✅ When to Use Use when: - The agent performs writes, deletes, sends, or other side-effecting operations - Operations vary in irreversibility and failure cost, making a uniform policy inadequate - A human review workflow exists and latency budget allows approval wait time Don't use when: - All operations are read-only with no side effects - Failure cost is uniformly low and rollback is easy - Latency requirements are too tight for human involvement ⚠️ Pitfalls - Never let the LLM classify risk. Classification belongs in deterministic code or a policy engine - Approval-pending state must be persisted; otherwise process restarts lose queued operations - Design for conditional approval (parameter modification) to avoid costly reject-and-resubmit loops 🔧 Implementation Approach - Classify risk using a static table (tool name x action) or a policy engine -- never the LLM itself - Compute risk score as (1 - reversibility) x failure_cost and map to three tiers (auto / approval / forbidden) via thresholds - Persist approval-pending state in a durable store (queue + persistence) so process restarts don't lose queued operations - Default approval timeout behavior to rejection (safe side) rather than auto-approval - Externalize risk policies as declarative config (YAML) so rules can be added or changed without code deploys #AIAgents# #SoftwareArchitecture#

# Decision Points in AI Agent Development # Orchestration vs Choreography 🎼 🎯 The Hook When coordinating multiple agents or services, you face a fundamental choice: place a conductor at the center, or let each component dance autonomously? Orchestration means a central authority controls everything. Choreography means each component reacts to events on its own. The locus of control is fundamentally different, and it shapes fault handling, auditing, debugging, and scaling in entirely different ways. 📋 Overview In orchestration, a central orchestrator defines the entire workflow, invokes components sequentially or in parallel, aggregates results, and decides the next step. Temporal, Airflow, and LangGraph's Supervisor pattern are typical implementation platforms. In choreography, each component subscribes to events on an event bus (Kafka, EventBridge, etc.), autonomously processes events it cares about, and publishes new events. No central entity knows the overall control flow. 🔍 Decision Points The primary axis is **accountability**. 🏛️ **Favor orchestration when**: - You need to explain the overall flow and decision rationale at each step after the fact - Strict sequencing constraints exist (review → approval → execution) - LLM outputs need validation or transformation before passing to the next step - Central budget management (tokens, time, cost) is required - Component count is roughly 10 or fewer 🌊 **Favor choreography when**: - High throughput and high scale are required and a central point becomes a bottleneck - Many teams independently develop and deploy components - "Reacting to events" is the primary processing pattern (notifications, logging, async aggregation) - Strict execution order tracking is unnecessary 💡 Key Details 🟢 **Orchestration excels in clarity of control and auditability.** The entire workflow is defined in one place, so "how far have we progressed" and "why was this step executed" are always clear. Recovery from failures pinpoints exactly which step failed and resumes from there. Agent-specific advantage: LLM outputs can be validated before proceeding to the next step, blocking hallucination propagation at each stage. 🟡 **Choreography excels in loose coupling and scalability.** Components share only event schemas and know nothing about each other's existence. Adding a new component is just adding an event subscription -- no changes to existing components. Each component scales independently, with the event bus acting as a buffer to absorb temporary load spikes. ⚖️ Trade-offs | Dimension | Orchestration | Choreography | |---|---|---| | Overall state visibility | Always clear 🟢 | Requires event log correlation 🔴 | | Auditability | High (causal chains are traceable) | Low (distributed tracking needed) | | Scalability | Central becomes bottleneck | Independent component scaling | | Coupling | High (changes concentrate on center) | Low (schema sharing only) | | Hallucination control | Validate at each step | Each component needs own guardrails | | Team independence | Orchestrator changes cause conflicts | Independent development and deployment | 🛠️ Use Cases 🔵 **Orchestration fits**: Business systems, regulated processes, review-approval workflows, processing requiring LLM output validation. Environments demanding accountability. 🔴 **Choreography fits**: High-throughput event-driven processing, notification/log aggregation/analytics pipelines. Large-scale systems where many teams independently develop components. 📌 **Default strategy**: For business systems, orchestration (centralized) is the default. In systems involving AI agents, a central entity that controls and validates LLM's probabilistic outputs is critical for both safety and auditability. A practical compromise is "centralized core, event-driven periphery" -- the orchestrator manages the main business flow while peripheral async processing (logging, notifications, analytics) is loosely coupled via events. #AIAgents# #SoftwareArchitecture#

# Decision Points in AI Agent Development # Single Agent vs Multi-Agent 🤖 🎯 The Hook Multi-agent systems look impressive. But "impressive" is not an architecture rationale. The real question: does the benefit of separating specializations outweigh the coordination cost? There is no "just a little multi-agent." The boundary between single and multi is discontinuous -- you either introduce coordination infrastructure or you don't. 📋 Overview A single agent is one LLM instance holding all tools, all context, and all permissions, processing the entire task. Control flow completes within a single loop with no inter-agent communication. A multi-agent system has multiple specialized Workers coordinated by a Supervisor. Each Worker holds only the tools and context for its domain. The Supervisor handles task decomposition, budget allocation, and result aggregation. 🔍 Decision Points Two axes drive this choice. 1️⃣ **Task variability and specialization separation** - Tool set fits within 20 tools and one context window → Single - Specialization axes split into 2+ distinct domains where mixing creates noise → Multi - Parallel execution of independent subtasks helps meet latency budgets → Multi 2️⃣ **Cost sensitivity** - Cannot tolerate increased LLM call volume → Single - Coordination overhead (Supervisor token consumption, error propagation design) is less than the benefit of separation → Multi 💡 Key Details 🟢 **Single agent excels in simplicity and cost efficiency.** Debugging stays within one context window. Testing validates one agent's inputs and outputs. No state-sharing problems, no consensus needed. As long as everything fits in the context window, all information is available to a single inference with zero information transfer loss. In a multi-agent setup, Supervisor task decomposition + independent Worker LLM calls + result aggregation can inflate token consumption by 3-5x. 🟡 **Multi-agent excels in three areas: specialization isolation, minimal permissions, and parallel execution.** Each Worker's context window contains only domain-specific information, improving inference accuracy. Permissions are granted per Worker under least-privilege principles -- if one Worker is compromised, damage is contained. Independent subtasks can run in parallel to save latency. ⚖️ Trade-offs | Dimension | Single Agent | Multi-Agent | |---|---|---| | Debugging | Contained in one context 🟢 | Distributed tracing required 🔴 | | Cost | One LLM loop | 3-5x token consumption | | Inference accuracy | Degrades beyond ~20 tools | Improves with specialization | | Security | All permissions concentrated | Per-Worker permission isolation | | Parallelism | Not possible | Available for independent subtasks | 🛠️ Use Cases 🔵 **Single agent fits**: Information retrieval, summarization, classification with a limited tool set. Cost-sensitive projects. Tasks where specialization has only one axis. 🔴 **Multi-agent fits**: Code generation + test execution + review, where specialization axes are clearly distinct. Tasks requiring different domain expertise (legal + technical). Systems where security demands permission isolation. 📌 **Default strategy**: Start with a single agent. Coordination costs in multi-agent systems are consistently underestimated. When bottlenecks become clear -- context overflow, coarse permissions, latency limits -- add the minimum Workers needed to resolve them. Keep Worker count at 2-5; beyond that, evaluate coordination cost growth carefully. #AIAgents# #SoftwareArchitecture#