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# 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#

# Decision Points in AI Agent Development # Synchronous vs Asynchronous ⚡ 🎯 The Hook The very first architectural decision for your LLM agent: should it respond synchronously or asynchronously? Get this wrong, and you'll be rebuilding your entire architecture later. Traditional web APIs assumed ~100ms responses. Agents? Their latency spans seconds to tens of minutes. That variance makes sync/async the unavoidable first fork. 📋 Overview Synchronous means the client sends an HTTP request, holds the connection, and receives the result as the response. State lives in-request scope -- no job queues, no checkpoint stores. Asynchronous means the client gets a job ID immediately (HTTP 202 Accepted), and background workers handle the processing. Results arrive via polling, webhooks, SSE, or WebSockets. Execution state is persisted as checkpoints in external stores. 🔍 Decision Points Two variables drive this decision. 1️⃣ **Latency budget** is the primary axis. It comes down to whether LLM p99 latency exceeds client wait tolerance. - Within 5-10s (user-facing) or 30s (API integration) → Synchronous - Exceeds the above, or duration is unpredictable → Asynchronous - Bimodal distribution (short path succeeds, long path fails) → Hybrid 2️⃣ **Reversibility / retry cost** is the secondary axis. - Failure permits full restart → Synchronous is sufficient - Mid-process resume needed; restart is costly → Asynchronous If human approvals occur mid-flow, holding a synchronous connection becomes impractical. Asynchronous becomes mandatory. 💡 Key Details 🟢 **Synchronous shines in simplicity.** Debugging follows stack traces. Testing validates function inputs and outputs. Deployments treat it as stateless HTTP. Fewer moving parts mean easier troubleshooting. It suits text classification, information extraction, basic Q&A, summarization, and structured output -- tasks completing in one LLM call plus 0-2 lightweight tools. 🟡 **Asynchronous shines in durability and scalability.** Processing time has no ceiling. Failed workers resume from the last checkpoint. Human approvals (minutes to days) don't consume workers. Horizontal scaling requires only adding queue workers. But it demands job queues (SQS, Redis Streams, Temporal), checkpoint stores, result stores, and notification mechanisms. Debugging requires distributed tracing across request-to-queue-to-worker-to-result flows. ⚖️ Trade-offs | Dimension | Synchronous | Asynchronous | |---|---|---| | Infrastructure complexity | Low (HTTP only) | High (queues + stores + notifications) | | Debugging | Stack traces | Distributed tracing required | | Fault tolerance | Crash = total loss | Resume from checkpoint | | Scaling | Connection holding is the bottleneck | Add workers horizontally | | Human approval | Impractical | Natural fit | 🛠️ Use Cases 🔵 **Synchronous fits**: Text classification, extraction, simple Q&A, summarization, structured output generation. Tasks that reliably complete in seconds. 🔴 **Asynchronous fits**: Multi-tool chains, cross-SaaS processes, human-approval workflows, tasks exceeding 30 seconds. 🟣 **Hybrid**: An internal async pipeline that auto-switches -- returns synchronous responses within the threshold, returns job IDs when exceeded. Especially effective for bimodal latency distributions. 📌 **Default strategy**: When in doubt, start synchronous. Migrating from sync to async when latency exceeds limits is far safer than the reverse. Sync-to-async migration is straightforward; async-to-sync rollback leaves unnecessary infrastructure behind. #AIAgents# #SoftwareArchitecture#

# Decision Points in AI Agent Development # Trace Sampling Rate 🎯 The Hook Are you recording every single agent trace? Or none at all? The right question isn't "all or nothing" -- it's "what deserves full recording?" A single AI agent request generates thousands to tens of thousands of tokens worth of trace data. Record everything and observability costs explode. Record nothing and debugging becomes impossible. Conditional sampling solves this dilemma. 📋 Overview Trace sampling rate controls what proportion of agent execution traces -- each LLM call, tool execution, and decision step -- are collected and stored. At 100%, every request is traced. At 1%, only 1 in 100. AI agent traces are qualitatively different from traditional web service logs. Including full prompts, full outputs, tool arguments and return values, and intermediate reasoning steps, a single request can generate tens to hundreds of KB of data. Moreover, LLM outputs are probabilistic, so the assumption that "replaying the same input reproduces the same behavior" does not hold. This makes traces irreplaceable for post-incident analysis. 🔍 Decision Points Sampling rate balances accountability and cost_sensitivity, but the most critical design choice is conditional sampling 🎯 Rather than a uniform probability, vary the rate based on request attributes. Priority order for sampling decisions: 1. Requests with errors/exceptions → 100% (mandatory) 2. Requests with HITL (human-in-the-loop) events → 100% 3. Requests involving high-risk operations (side effects, irreversible) → 100% 4. Requests exceeding P95 latency → 100% 5. Requests exceeding cost thresholds → 100% 6. Successful requests → sample at 1-10% 💡 Key Details Reference values 📊 - Error/exception occurred: 100%. Essential for debugging non-reproducible failures - HITL triggered (human approval/escalation): 100%. Required for post-hoc verification of approval decisions - High-risk operations (money transfers, data deletion): 100%. Mandatory for auditing irreversible actions - Latency exceeding P95: 100%. Needed for root cause analysis of performance degradation - Successful and low-risk: 1-10%. Sufficient for statistical quality monitoring - Dev/staging environments: 100%. Full recording where cost isn't a concern Control trace granularity independently from sampling rate 📦 Even for fully recorded requests, store full prompts in the cold tier and metadata (model name, token count, latency, status) in the hot tier. ⚖️ Trade-offs Too low a sampling rate makes failure reproduction impossible 🔍 LLM outputs are probabilistic -- replaying the same prompt won't necessarily reproduce the same error. You also risk missing gradual quality degradation, failing audit requirements, and delayed detection of cost anomalies. Too high a rate and observability costs can rival or exceed production LLM call costs 💸 Performance impact from synchronous trace collection, PII proliferation risk, and signal drowning in noise are additional concerns. Start with a high sampling rate (50-100%) at launch, confirm system stability, then gradually reduce the rate for successful requests. 🛠️ Use Cases Consider tail-based sampling instead of head-based 🔄 Head-based sampling (decided at request start) is simpler to implement, but you can't know upfront whether an error will occur. Tail-based sampling (decided after completion) lets you make decisions based on outcomes, though it requires temporarily buffering intermediate data. Ensure correlation ID (trace ID) propagation is airtight 🔗 In multi-step agent executions, without a consistent trace ID from the first request to the last tool call, you end up with fragmented traces. IDs are especially prone to breaking across async processing and message queues. Combine with hot/cold tier separation for efficiency. Store full trace data in the cold tier for sampled requests and metadata only in the hot tier for everything else -- a practical architecture that balances observability with cost control. #AIAgents# #SoftwareArchitecture#

# Practices for Embedding AI Agents in Software # Context Budget Allocator 🎯 The Hook "Just put everything in the context window" sounds reasonable until your costs spike, your system instructions get pushed out, and the LLM ignores the most important retrieved documents buried in the middle. 🔥 The Problem In RAG-powered agents, search results, conversation history, system instructions, and long-term memory all compete for the same finite token window. More input means higher cost but not necessarily better output. As conversations grow, system instructions shrink proportionally and behavior degrades. The "Lost in the Middle" phenomenon means information placed in the center of a long context gets less attention than content at the beginning or end. 💡 The Pattern Divide the context window into named slots (system instructions, retrieval, history, memory) each with a maximum token ratio and priority. Reserve system instructions as a non-compressible fixed slot at 10-20%. Cap retrieval results at a reranked top-k of 3-8 documents. Compress conversation history via summarization when window usage exceeds a threshold. Arrange content to counter Lost in the Middle: critical information first, recent user input last. The higher the cost sensitivity, the tighter the top-k, the lower the compression threshold, and the shorter the history retention. ✅ When to Use Use when: - RAG or memory is active and candidate content could exceed 50% of the model's context window - Cost sensitivity is medium or higher, with token volume affecting both cost and inference latency - Multi-turn conversations accumulate history that crowds out other content types Don't use when: - Input is just system instructions plus a single user message, fitting within 30% of the window - Using a long-context model with input under 20% of the window and low cost sensitivity ⚠️ Pitfalls - Never compress system instructions. Losing tool definitions or safety rules breaks agent behavior entirely - Raw top-k without reranking has low signal density. Retrieve 20 candidates, rerank to 3-8 with a cross-encoder - Summarization is lossy. Key decisions and proper nouns can vanish. Combine with keyword extraction to preserve critical terms 🔧 Implementation Approach - Model the context window as named slots (system/user/retrieval/history/memory) with a struct defining max token ratio, priority, and compressibility per slot - Reserve system instructions as the highest-priority non-compressible fixed allocation, then distribute remaining budget to other slots in descending priority order - Cap retrieval content by reranking vector search candidates with a cross-encoder before fitting within the slot budget, maximizing signal density - Trigger summarization compression on the history slot when it exceeds budget, combining with keyword extraction to prevent loss of critical terms #AIAgents# #SoftwareArchitecture#

# Decision Points in AI Agent Development # Self-Correction Retry 🎯 The Hook When your LLM output breaks, are you just blindly resending the same prompt? Self-correction retry feeds back *what went wrong* so the model can fix itself. But here's the catch: beyond 3 retries, improvement almost never happens. Knowing when to stop is as important as knowing when to retry. 📋 Overview Self-correction retry controls how many times you re-prompt an LLM after its output violates a schema or falls short on quality, injecting error details into context each time. This is fundamentally different from network retries (resending the identical request). By providing specific feedback about what went wrong, you give the model a real chance to produce correct output on the next attempt. LLM outputs are probabilistic -- missing JSON brackets, out-of-range values, and incomplete responses happen routinely. Self-correction retry is one of the most practical strategies for handling these errors. 🔍 Decision Points The primary driver is failure_cost: how much damage does a bad output cause downstream? Applying the same retry count to all errors is wasteful, so differentiate by error type. Syntax-level errors (malformed JSON, type mismatches) almost always resolve with a single feedback round. Semantic-level errors (out-of-range values, nonexistent ID references) may improve with feedback, but if the second attempt fails, it's a structural problem. Quality-level errors (incomplete answers, missing information) are subjective and rarely improve through retries -- invest in prompt engineering instead. 💡 Key Details Reference values to keep in mind 📊 - General case: 1-3 retries. If no improvement after 2, likely a structural issue - Structured output (JSON Schema): 1-2 retries. Using response_format yields high first-attempt success rates - High failure_cost domains (financial, legal, medical): 2-3 retries. Escalate to humans if quality plateaus - Low failure_cost domains: 0-1 retries. If fallbacks exist, fail fast for efficiency Error messages should be short and specific 🎯 Not "output is invalid" but "the delivery_date field is a past date; please specify a future date." Spell out what's wrong and what's expected. Keep error messages under ~200 tokens since they consume context budget. ⚖️ Trade-offs Too few retries means discarding fixable errors. Throwing away output that's only missing a closing bracket? That's wasteful. Too many retries and costs explode ⚡ If one LLM call takes 30 seconds, 5 retries means 2.5+ minutes of waiting. Each retry appends error messages to the context, causing cumulative token growth. Worst case, you hit context length limits and trigger an entirely different failure mode. If the same error type appears twice in a row, question the prompt before attempting a third retry. Repeated identical errors signal that the LLM cannot produce correct output with the current prompt-schema combination. 🛠️ Use Cases JSON schema violations: Provide specific error feedback; typically fixed in 1 retry. Using Structured Outputs (response_format) eliminates most syntax-level retries entirely. Business rule violations (e.g., delivery date in the past): Include concrete constraints and current state in feedback. If 2 retries don't help, pivot to prompt redesign. Quality shortfalls ("analyze from 5 perspectives" returns only 3): Try once; if no improvement, accept partial results or split the prompt into smaller generation tasks. Persistent identical errors: Consider lowering temperature, simplifying the prompt, trying a different model, or escalating to a human operator 🔄 #AIAgents# #SoftwareArchitecture#

# Practices for Embedding AI Agents in Software # Dry-run & Commit / Plan-then-Apply 🎯 The Hook An LLM hallucinated a payment amount and your agent executed it. With a dry-run step, you would have caught it before any money moved. 🔥 The Problem LLMs can hallucinate parameters, and tool calls carry real-world side effects. When these two combine, an agent may execute an irreversible operation with a nonexistent resource ID or a wildly wrong amount. Without a preview step, humans have no way to inspect what the agent intends to do until the damage is done. 💡 The Pattern Split side-effect operations into two phases: plan (dry-run) and apply (commit). In the dry-run phase, compute a diff of what would change without modifying any state. Present the diff for approval, then execute the commit only after authorization. Graduate approval by risk level: human approval for high-risk, automated policy checks for medium, auto-approve for low. Attach a TTL to each plan and re-verify preconditions at commit time to guard against state drift between phases. ✅ When to Use Use when: - The agent executes irreversible operations (data deletion, external API writes, billing) - Mistakes carry financial, legal, or operational consequences - A few seconds to minutes of latency for review is acceptable Don't use when: - All operations are read-only - All operations are reversible and low-cost (e.g., chat response generation) - Latency constraints are too tight to allow an approval step ⚠️ Pitfalls - TOCTOU: state can change between plan and commit. Always re-verify preconditions at commit time - When external APIs lack a dry-run mode, substitute with parameter validation and simulation, and clearly mark the diff as "estimated" - If the commit endpoint can be called without a valid plan ID, the entire dry-run can be bypassed 🔧 Implementation Approach - Structure tool execution as a three-phase pipeline: dry-run (compute diff only), approval (risk-based), and commit (execute), with each phase as a separate endpoint - Include before/after values, blast radius, rollback procedures, and a precondition state hash in the plan object, re-verifying preconditions at commit time to counter TOCTOU - Require both a valid plan ID and an approval token as mandatory parameters on the commit endpoint, making dry-run bypass structurally impossible - Set a TTL on each plan and force re-planning if expired, preventing execution based on stale diffs #AIAgents# #SoftwareArchitecture#