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Give a Code-Interpreter Agent Your Tools, Keep Approval Gates

Building a code interpreter agent with approval gates means letting the model write and run one program over your tools while every mutating call it makes still stops at a human — and gets counted against a hard per-run budget. That combination is the whole point of this guide. The code-interpreter (codeact) pattern itself is easy; the interesting engineering is the governance you refuse to give up when you hand a model an execution surface. This post is scoped to exactly that: enable agent_pattern="code-action", wrap the tools that change state with an approval gate, keep a hard max_tool_calls cap on the generated program, and pick the gVisor backend when the input is untrusted.

  • in MCP
  • 10 min read

How to Compose Multiple MCP Servers Into One Gateway

To compose multiple MCP servers into one gateway, you mount each team's server behind a namespace prefix so an agent connects to a single endpoint and discovers one clean toolbelt — pay_charge, usr_get_user, rpt_revenue — instead of juggling three URLs, three auth handshakes, and three tool lists it has to deduplicate itself. That much is table stakes; several frameworks can prefix-and-merge. The part that actually decides whether a gateway survives contact with production is what happens at the seam: can you filter what each client sees across the composed surface based on who is asking, and can two mounted servers ship different versions of the same tool without a name collision?

Set a Confidence Threshold to Stop or Escalate an Agent

A useful confidence threshold to stop an AI agent is only trustworthy when the confidence comes from an independent judge, not from the agent's own claim that it finished. That distinction is the whole game. The moment you let an agent stop itself the instant it says "done," you have built a rubber stamp: the model declares victory, the loop halts, and nobody checks whether the mission was actually accomplished. Flip the failure around and it is just as bad — an agent with no completion signal at all runs on its trigger forever, re-doing finished work and burning tokens. This post shows the middle path Promptise Foundry makes first-class: a mission whose completion and escalation are both gated on a separate evaluator's numeric confidence — auto_complete on a confident success, escalate below your threshold, and a hard timeout_hours backstop so a shaky agent gets a human, not a rubber stamp.

Add Budget + Health Kill-Switches to a Runtime Agent

To configure AI agent budget and health kill-switches you should not need a monitoring side-project, a supervisor loop, or a scatter of try/except blocks around every tool call — you need one config block on the process. This is the copy-paste recipe, not the why-you-need-it explainer: paste one ProcessConfig (or one .agent YAML manifest), fill in your per-run and daily limits, your irreversible-action cap, the four behavioral-health detectors, pick pause / stop / escalate, point a webhook at Slack, and ship a governed, self-limiting agent in a single file. The runtime enforces the whole declaration for you, around every invocation, out-of-band. Below is the exact block.

Connect Your AI Agent to MCP Tools with Promptise

To connect an AI agent to MCP tools in production, you need two things: reliable auto-discovery so you never hand-wire schemas, and a way to stop a large tool catalog from bloating every prompt. Most tutorials give you the first and quietly skip the second — then your token bill climbs as you add servers. This post shows the production path with Promptise: point the agent at your MCP servers, let it discover every tool, and flip tool optimization to SEMANTIC so local embeddings select only the tools each query actually needs. By the end you'll have a working config, the air-gapped local-embeddings variant, and a clear sense of when you don't need any of it.

Context Window Management for LLM Agents, Explained

Context window management is the difference between an agent that answers the question you asked and one that silently drops it. Every call your agent makes stuffs a system prompt, tool definitions, retrieved memory, and conversation history into a single request — and when that request outgrows the model's window, something has to go. Do nothing and the model either errors out or truncates from wherever the framework happened to stop, which is often the user's latest message. By the end of this post you'll know how to count tokens exactly, assign priorities to every piece of context, and trim gracefully so the parts that matter always survive.

Conversation Persistence for LLM Agents in Python

Conversation persistence is the difference between a demo and a product: without it, every LLM agent forgets the last message the moment the process restarts, and a multi-user app happily serves one person's thread to another. Most tutorials hardcode a single database and quietly skip session ownership, so you inherit a rewrite the day you move from SQLite to Postgres — and a security bug you won't notice until someone reports it. This post shows how to persist chat history in Python behind one ConversationStore protocol, swap backends without touching application code, and enforce session ownership so users can only load their own conversations.

CrewAI Alternative: When to Switch (and When Not)

If you're searching for a CrewAI alternative, you probably already have a working crew — a set of role-playing agents that collaborate on a task — and you're now hitting the wall between "impressive demo" and "service other people pay for." That wall is rarely about reasoning quality. It's about isolation, human approval on risky actions, and an audit trail you can defend. This post is the honest version: where CrewAI is the right tool, and the specific moment where switching to Promptise Foundry earns its keep. By the end you'll be able to tell which side of that line your project is on, and stand up a tenant-isolated MCP server if you're on ours.

How to Build a Customer Support AI Agent, Step by Step

A production customer support ai agent is not a single-turn question-and-answer bot with a vector store bolted on. Real support is a conversation that changes shape as it goes — you greet, you figure out what's actually wrong, you look up the order, and only then do you resolve or escalate. Most tutorials ship the easy 20% (retrieve an article, paste it into a prompt) and skip the parts that break in production: behavior that shifts across turns, a reply that gets validated before it sends, escalation rules, and history that survives a restart. By the end of this guide you'll have built all of those with Promptise Foundry, and you'll be able to run the whole thing with your own OPENAI_API_KEY.

How to Cut Token Cost for a Multi-Tenant AI Agent

To cut token cost, multi-tenant AI agent deployments have to fight three inefficiencies at once — a leaky shared cache, unbounded tool-loop transcripts, and the full set of tool schemas resent on every call — because one agent serving many customers multiplies each of them per tenant. A waste that is annoying for a single-user demo becomes the dominant line on your bill when a thousand tenants hit the same code path. This post is the hub that connects the three cost levers Promptise Foundry ships as first-class, composable primitives on one build_agent() call — a per-tenant semantic cache, a context_scope="ledger" transcript, and optimize_tools — and then shows you how to prove the savings with built-in observability rather than trust a marketing number.

Set a Daily Budget for an Autonomous AI Agent

Setting a real daily budget for AI agents means capping what an agent does across a whole day, not what it does inside one function call — and that distinction is the entire problem with the guardrails most frameworks ship. A per-run cap like max_iterations counts to N and resets to zero the moment the next invocation starts. That is fine for a chatbot a human is watching. It is worthless for an agent on a five-minute cron, because the thing you actually want to bound — total tool calls, total cost units, total runs — is spread across hundreds of invocations that never see each other's counters. This post shows how Promptise Foundry's BudgetConfig daily scope gives a long-lived, trigger-driven process a genuine 24-hour ceiling: max_tool_calls_per_day, max_cost_per_day, max_runs_per_day, and a configurable daily_reset_hour_utc, all tracked across every cron-, webhook-, and event-driven run.

Data Analysis AI Agent: Natural Language to SQL

A data analysis AI agent turns a plain-English question — "which region grew fastest last quarter, and by how much?" — into SQL, runs it, cross-references the results, and hands back an exact number. The catch is that a generic ReAct loop is dangerous here: when it has to join two tables and do arithmetic across the results, it will confidently invent a figure that looks plausible and is simply wrong. By the end of this post you'll know why that happens, and how to wire a plan-execute-verify reasoning pattern plus a bounded, ledger-scoped tool loop so your agent checks its math before it speaks.

Delete One Tenant's Data for GDPR, Not the Rest

Delete one tenant's data for GDPR right-to-erasure and you inherit a deceptively hard requirement: remove exactly that customer's records from the semantic cache, the vector store, and long-term memory — and nothing belonging to anyone else. On a shared multi-tenant agent, "their data" is smeared across at least three stores, each keyed differently, and a right-to-erasure request gives you a legal deadline to prove you got all of it. This post shows how Promptise Foundry turns that into one call per surface — cache.purge_user("alice", tenant_id="acme") and provider.purge_user(caller.isolation_key) — where the tenant is baked into the key, so the purge matches exactly what was stored.

Deploying AI Agents to Kubernetes with Promptise

Deploying AI agents to Kubernetes is where most agent projects stall: the tool server runs fine on your laptop, but the cluster wants HTTP endpoints, real health probes, and metrics it can scale on — none of which a bare python server.py gives you. Promptise closes that gap with pieces you can wire in a few lines: a HealthCheck for dependency-aware readiness, PrometheusMiddleware for metrics, and promptise serve --transport http to run the server without boilerplate. By the end of this post you'll have a copy-paste path from a local MCP server to a running Kubernetes Deployment with liveness, readiness, and startup probes and a scrapeable /metrics endpoint.

How to Detect a Tampered AI Agent Audit Trail

To detect a tampered agent audit trail you need one property a plain JSONL file or stdout stream can never give you on its own: a way to prove, after the fact, that no record was edited, deleted, or reordered. A log your agent appends to is just bytes on disk. Anyone with write access — a compromised process, a careless migration script, an insider covering their tracks — can rewrite a line, drop an entry, or truncate the file, and nothing about the surviving data reveals the change. This post is the operational verification workflow, not the theory: exactly what Promptise's verify_chain() checks, how the break localizes to the specific entry an insert, edit, or delete touched, and how to wire the check into CI, a periodic integrity job, and an incident-response runbook so you can prove a clean chain on demand.

DevOps AI Agent: Autonomous CI/CD Pipeline Monitoring

A real devops ai agent is not a chat window you paste stack traces into — it's a long-lived process that wakes up when your pipeline emits an event, investigates on its own, fixes what it safely can, and only pages a human for the genuine criticals. That daemon shape is where most tutorials stop and where production teams actually start. By the end of this post you'll understand the pattern SRE teams use, see a webhook-triggered agent process wired with budget, health, and mission governance, and know how the journal replays state after a crash so a restart doesn't lose the incident it was working.

Build a PII-Safe Document Processing AI Agent

A document processing ai agent is one of the highest-value things you can build with an LLM — invoices, contracts, lab results, and onboarding forms are exactly the unstructured text that models are good at, and exactly the data your compliance team loses sleep over. Two failure modes break these pipelines in production: sensitive data leaks out in an extracted response, and generated parsing code runs with full access to your host. This build closes both. By the end you'll have a working pipeline where every extracted output is scanned for PII before it leaves the agent, and any code the agent writes to parse a file runs inside a locked-down Docker sandbox with no network.

Does LangChain Support Multi-Tenancy? The Honest Answer

If you are asking does LangChain support multi-tenancy, the honest answer is: not as a first-class concept — LangChain hands you excellent building blocks (memory stores, caches, retrievers, rate limiters), but there is no tenant identity that flows through them, so per-tenant isolation is something you wire and enforce yourself on every call. That is not a knock; it is a scope decision. This post is precise about exactly what LangChain gives you today, where the classic "two customers both named alice" leak comes from, and how Promptise Foundry turns tenancy from a filter you can forget into a structural invariant.

  • in Runtime
  • 8 min read

Durable Execution for AI Agents in Python

Durable execution for AI agents is the property that lets a long-running, self-triggering agent survive a crash and come back exactly where it left off — same context, same counters, same lifecycle state — instead of restarting from zero. The term "durable execution" comes from the workflow world, where engines like Temporal, Restate, and DBOS made it famous. This page explains what the phrase means once your unit of work stops being a deterministic workflow and becomes a stateful agent, why the existing tools only solve half of it, and how Promptise Foundry closes the gap with a supervised process, an append-only journal, and a replay engine. It is the hub for the whole durability cluster, so it stays at the architecture altitude and points you at the deep dives for each mechanism.

Encrypt Your LLM Response Cache at Rest in Redis

Learning to encrypt LLM cache at rest starts with reckoning with what a semantic cache actually is on disk: a running log of every prompt a user typed and every answer your model gave back, sitting in Redis as plaintext JSON. The feature that makes the cache cheap — remembering answers so you can serve them again — is exactly what turns it into a liability. An RDB snapshot copied to a backup bucket, a replica tapped by a read-only credential, a KEYS * from a misconfigured requirepass, or an instance accidentally bound to a public interface: any one of those hands over conversations verbatim. Promptise Foundry ships a one-flag answer to this. Set encrypt_values=True on SemanticCache and the prompt-and-answer payload is encrypted application-side with AES (via Fernet) before it ever reaches Redis, keyed by PROMPTISE_CACHE_KEY so it stays readable across restarts. This post walks the threat, the exact toggle, and how to verify ciphertext is really what landed at rest.