Self-Correcting Agents: Reflexion, CRITIC, and ReAct Loops Compared

Why Self-Correction Stopped Being Optional

The frontier-model accuracy gains between 2024 and 2026 came as much from inference-time correction as from raw pretraining. The same model with no correction loop and the same model with a tuned correction loop differ by 8 to 15 points on hard tasks. The 2026 question is not whether to add a correction loop, but which pattern to use.

Three patterns dominate: Reflexion, CRITIC, and the ReAct loop with explicit verifier. Each one has a different mental model of "what was wrong" and a different cost profile.

ReAct With Verifier

flowchart LR
    T[Thought] --> A[Action]
    A --> O[Observation]
    O --> V{Verifier OK?}
    V -->|Yes| T2[Next Thought]
    V -->|No| Fix[Repair Thought]
    Fix --> A
    T2 --> A2[Next Action]

The original ReAct loop interleaves thoughts with actions. The 2026 upgrade adds an explicit verifier (often a smaller, fast LLM or a deterministic check) that gates each observation. Cheap, low-overhead, well-suited to tool-using agents where each tool result has objective acceptance criteria.

Wins when: tool outputs are verifiable (compiles? passes lint? matches schema?). Fails when: errors are semantic and only visible at the trajectory level.

CRITIC

CRITIC adds an external knowledge-grounded critic step after each output. The critic compares the model's claims to a ground-truth source — often via web search, a database, or a code interpreter — and emits criticism that feeds back into the next attempt.

flowchart LR
    P[Proposal] --> C[Critic: ground claims to evidence]
    C -->|Issues found| R[Refine]
    C -->|All grounded| Out[Output]
    R --> P

Wins when: factual hallucination is the failure mode (Q&A, summarization, research agents). Fails when: the ground-truth source itself is wrong or unavailable, or when the critic is the same model as the proposer (self-grading is unreliable on hard problems).

Reflexion

Reflexion sits at the trajectory level. After a complete run, the agent generates a verbal self-reflection on what went wrong, stores it in memory, and starts the next run with that reflection in context. It targets the case where individual steps look fine but the trajectory is wrong.

flowchart TB
    Run1[Run 1: fail] --> Refl[Self-Reflection]
    Refl --> Mem[(Reflection Memory)]
    Mem --> Run2[Run 2: with reflection in context]
    Run2 --> Eval{Pass?}
    Eval -->|Yes| Done[Done]
    Eval -->|No| Refl

Wins when: failure is structural ("I should have asked the user for X first") and a fresh attempt is cheap. Fails when: tasks are non-resettable (you cannot retry a sent email) or the reflection itself hallucinates.

Combining Them

The strongest 2026 production agents use all three at different layers:

• ReAct with verifier at the step level — cheap, fast, catches most errors
• CRITIC at sub-task boundaries — invoked when the agent is about to commit a side effect
• Reflexion between full task attempts — only on retry-safe tasks, not first attempts

Cost matters. Reflexion is the most expensive because it can multiply your token count by the number of retries. CRITIC adds a fixed overhead per checkpoint. ReAct verifiers are usually small models so the overhead is sub-10 percent.

A 2026 Reference Implementation

OpenHands, Devin reproductions, Anthropic Claude Code, and Cursor's Composer all implement variants. The common structure is:

1. Each tool call has an attached verifier (compiler error? lint failure? schema mismatch?). Failures route back to the same step.
2. Side-effect-bearing tools (file write, email, payment) require a CRITIC pass against the original goal.
3. Whole-task failures emit a reflection that is stored in episodic memory and surfaced at the start of the next attempt.

Sources

• Reflexion paper (Shinn et al.) — https://arxiv.org/abs/2303.11366
• CRITIC paper (Gou et al.) — https://arxiv.org/abs/2305.11738
• ReAct paper (Yao et al.) — https://arxiv.org/abs/2210.03629
• OpenHands repository — https://github.com/All-Hands-AI/OpenHands
• "Self-correction in LLMs" survey 2025 — https://arxiv.org/abs/2308.03188

Self-Correcting Agents: Reflexion, CRITIC, and ReAct Loops Compared — operator perspective

When teams move beyond self-Correcting Agents, one question shows up first: where does the agent loop actually end? In practice, the boundary is rarely the model — it is the contract between the orchestrator and the tools it calls. What works in production looks unglamorous on paper — small specialized agents, explicit handoffs, deterministic retries, and dashboards that show you tool latency before they show you token spend.

Why this matters for AI voice + chat agents

Agentic AI in a real call center is a different beast than a single-LLM chatbot. Instead of one model answering one prompt, you orchestrate a small team: a router that decides intent, specialists that own a vertical (booking, intake, billing, escalation), and tools that read and write to the same Postgres your CRM trusts. Hand-offs are where most production bugs hide — when Agent A passes context to Agent B, anything that isn't explicit in the message gets lost, and the user feels it as the agent "forgetting." That's why the systems that hold up under load are the ones with typed tool schemas, deterministic state stored outside the conversation, and a hard ceiling on tool calls per session. The cost story is just as important: a multi-agent loop can quietly burn 10x the tokens of a single-LLM design if you let it think out loud at every step. The fix isn't a smarter model, it's smaller agents, shorter prompts, cached system messages, and evals that fail the build when p95 latency or per-session cost regresses. CallSphere runs this pattern across 6 verticals in production, and the rule has held every time: the agent you can debug in five minutes will out-survive the agent that's "smarter" on a benchmark.

FAQs

Q: What's the hardest part of running self-Correcting Agents live?

A: Scaling comes from constraint, not capability. The deployments that hold up keep each agent narrow, cap tool calls per turn, cache the system prompt, and pin a smaller model for routing while reserving the larger model for synthesis. CallSphere's stack — 37 agents · 90+ tools · 115+ DB tables · 6 verticals live — is sized that way on purpose.

Q: How do you evaluate self-Correcting Agents before shipping?

A: Hard ceilings beat heuristics. A maximum step count, an idempotency key on every tool call, and a fallback to a deterministic script when confidence drops below a threshold are what keep the loop bounded. Evals that simulate noisy inputs catch the rest before they reach a real caller.

Q: Which CallSphere verticals already rely on self-Correcting Agents?

A: It's already in production. Today CallSphere runs this pattern in Salon and IT Helpdesk, alongside the other live verticals (Healthcare, Real Estate, Salon, Sales, After-Hours Escalation, IT Helpdesk). The same orchestrator code path serves voice and chat — the difference is the tool set the router exposes.

See it live

Want to see sales agents handle real traffic? Spin up a walkthrough at https://callsphere.ai/demo or grab 30 minutes on the calendar: https://callsphere.ai/book.